{ "short_name": "wdi", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "dataset": { "channel": "garden", "namespace": "worldbank_wdi", "short_name": "wdi", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. 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World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_Population1JanuaryBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Low-fertility scenario 1950-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Low_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Medium scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. 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World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_High_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Constant-fertility scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Constant%20fertility_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Demographic indicators", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/EXCEL_FILES/1_General/WPP2024_GEN_F01_DEMOGRAPHIC_INDICATORS_FULL.xlsx", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } } ] }, "ag_con_fert_pt_zs": { "title": "Fertilizer consumption (% of fertilizer production)", "description_from_producer": "Fertilizer consumption measures the quantity of plant nutrients and is calculated as production plus imports minus exports. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phosphate). Traditional nutrients--animal and plant manures--are not included. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. Fertilizer consumption as a share of production shows the agriculture sector's vulnerability to import and energy price fluctuation. Most fertilizers that are commonly used in agriculture contain the three basic plant nutrients-nitrogen, phosphorus, and potassium. Some fertilizers also contain certain micronutrients such as zinc and other metals that are necessary for plant growth. Materials that are applied to the land primarily to enhance soil characteristics (rather than as plant food) are commonly referred to as soil amendments.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Fertilizer consumption measures the quantity of plant nutrients, and is calculated as production plus imports minus exports. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. Fertilizer consumption as a share of production shows the agriculture sector's vulnerability to import and energy price fluctuation.\n\n\n\nFor the purpose of data dissemination, FAO has adopted the concept of a calendar year (January to December). Some countries compile fertilizer data on a calendar year basis, while others are on a split-year basis.\n\n\n\nFAO has revised the time series for fertilizer consumption and irrigation from 2002 onward. FAO collects fertilizer statistics for production, imports, exports, and consumption through the new FAO fertilizer resources questionnaire. In the previous release, the data were based on the total consumption of fertilizers, but the data in the recent release are based on the nutrients in fertilizers. Some countries compile fertilizer data on a calendar year basis, while others compile on a crop year basis (July-June). Previous editions of this indicator, Fertilizer consumption (100 g per ha of arable land), reported data on a crop year basis, but this edition uses the calendar year, as adopted by the FAO. Caution should thus be used when comparing data over time. The data are collected by FAO through annual questionnaires. FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n\n\nMost fertilizers that are commonly used in agriculture contain the three basic plant nutrients - nitrogen, phosphorus, and potassium. Some fertilizers also contain certain \"micronutrients,\" such as zinc and other metals that are necessary for plant growth. Materials that are applied to the land primarily to enhance soil characteristics (rather than as plant food) are commonly referred to as soil amendments. Fertilizers and soil amendments are largely derived from raw material, composts and other organic matter, and wastes, such as sewage sludge and certain industrial wastes.\n\n### Development relevance:\nFactors, such as the green revolution, have led to impressive progress in increasing crop yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\nIn many developed countries, excessive nitrogen fertilizer applications have sometimes led to pest problems by increasing the birth rate, longevity and overall fitness of certain agricultural pests, such as aphids. Further, excessive use of fertilizers emits significant quantities of greenhouse gases into the atmosphere. Over-fertilization of a vital nutrient can be detrimental, as \"fertilizer burn\" can occur when too much fertilizer is applied, resulting in drying out of the leaves and damage or even death of the plant. In many industrialized countries, overuse of fertilizers has resulted in contamination of surface water and groundwater.\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used.\n\n### Limitations and exceptions:\nThe FAO has revised the time series for fertilizer consumption and irrigation for 2002 onward. FAO collects fertilizer statistics for production, imports, exports, and consumption through the new FAO fertilizer resources questionnaire. In the previous release, the data were based on total consumption of fertilizers, but the data in the recent release are based on the nutrients in fertilizers. Some countries compile fertilizer data on a calendar year basis, while others compile on a crop year basis (July-June). Previous editions of this indicator, Fertilizer consumption (100 grams per hectare of arable land), reported data on a crop year basis, but this edition uses the calendar year, as adopted by the FAO. Caution should thus be used when comparing data over time.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n### Other notes:\nThe world and regional aggregate series do not include data from countries that no longer exist.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.CON.FERT.PT.ZS (https://data.worldbank.org/indicator/AG.CON.FERT.PT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.CON.FERT.PT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of fertilizer production", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of fertilizer production" } }, "ag_con_fert_zs": { "title": "Fertilizer consumption (kilograms per hectare of arable land)", "description_from_producer": "Fertilizer consumption measures the quantity of plant nutrients used per unit of arable land. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers (including ground rock phosphate). Traditional nutrients--animal and plant manures--are not included. For the purpose of data dissemination, FAO has adopted the concept of a calendar year (January to December). Some countries compile fertilizer data on a calendar year basis, while others are on a split-year basis. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Fertilizer consumption measures the quantity of plant nutrients, and is calculated as production plus imports minus exports. Because some chemical compounds used for fertilizers have other industrial applications, the consumption data may overstate the quantity available for crops. Fertilizer consumption as a share of production shows the agriculture sector's vulnerability to import and energy price fluctuation.\n\n\n\nMost fertilizers that are commonly used in agriculture contain the three basic plant nutrients - nitrogen, phosphorus, and potassium. Some fertilizers also contain certain \"micronutrients,\" such as zinc and other metals that are necessary for plant growth. Materials that are applied to the land primarily to enhance soil characteristics (rather than as plant food) are commonly referred to as soil amendments. Fertilizers and soil amendments are largely derived from raw material, composts and other organic matter, and wastes, such as sewage sludge and certain industrial wastes.\n\n\n\nFAO defines arable land as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow; land abandoned as a result of shifting cultivation is excluded.\n\n### Development relevance:\nFactors, such as the green revolution, have led to impressive progress in increasing crop yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many developed countries, excessive nitrogen fertilizer applications have sometimes led to pest problems by increasing the birth rate, longevity and overall fitness of certain agricultural pests, such as aphids. Further, excessive use of fertilizers emits significant quantities of greenhouse gas into the atmosphere. Over-fertilization of a vital nutrient can be detrimental, as \"fertilizer burn\" can occur when too much fertilizer is applied, resulting in drying out of the leaves and damage or even death of the plant. In many industrialized countries, overuse of fertilizers has resulted in contamination of surface water and groundwater.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used.\n\n### Limitations and exceptions:\nThe FAO has revised the time series for fertilizer consumption and irrigation for 2002 onward. FAO collects fertilizer statistics for production, imports, exports, and consumption through the new FAO fertilizer resources questionnaire. In the previous release, the data were based on total consumption of fertilizers, but the data in the recent release are based on the nutrients in fertilizers. Some countries compile fertilizer data on a calendar year basis, while others compile on a crop year basis (July-June). Previous editions of this indicator, Fertilizer consumption (100 grams per hectare of arable land), reported data on a crop year basis, but this edition uses the calendar year, as adopted by the FAO. Caution should thus be used when comparing data over time.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n### Other notes:\nThe world and regional aggregate series do not include data from countries that no longer exist.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.CON.FERT.ZS (https://data.worldbank.org/indicator/AG.CON.FERT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.CON.FERT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "kilograms per hectare of arable land", "short_unit": "kilograms", "display": { "shortUnit": "kilograms", "unit": "kilograms per hectare of arable land" } }, "ag_lnd_agri_k2": { "title": "Agricultural land (sq. km)", "description_from_producer": "Agricultural land refers to the land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies. Three components of the agricultural land are a) arable land - land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow, b) permanent pasture - land used for five or more years for forage, including natural and cultivated crops, and c) and under permanent crops - land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber; land under flowering shrubs, fruit trees, nut trees, and vines is included, but land under trees grown for wood or timber is not.\n\n\n\nAgricultural land is also sometimes classified as irrigated and non-irrigated land. In arid and semi-arid countries agriculture is often confined to irrigated land, with very little farming possible in non-irrigated areas. Land abandoned as a result of shifting cultivation is excluded from arable land.\n\n\n\nData on agricultural land are valuable for conducting studies on various perspectives concerning agricultural production, food security and for deriving cropping intensity among other uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n### Development relevance:\nAgricultural land covers more than one-third of the world's land area. In many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for understanding the structure of a country's agricultural sector; making economic plans and policies for food security; and deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used.\n\n### Limitations and exceptions:\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n### Other notes:\nAreas of former states are included in the successor states.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.AGRI.K2 (https://data.worldbank.org/indicator/AG.LND.AGRI.K2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.AGRI.K2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "sq. km", "short_unit": "sq. km", "display": { "shortUnit": "sq. km", "unit": "sq. km" } }, "ag_lnd_agri_zs": { "title": "Agricultural land (% of land area)", "description_from_producer": "Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Agriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural land is also sometimes classified as irrigated and non-irrigated land. In arid and semi-arid countries agriculture is often confined to irrigated land, with very little farming possible in non-irrigated areas. Land abandoned as a result of shifting cultivation is excluded from Arable land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area.\n\n### Development relevance:\nAgricultural land covers more than one-third of the world's land area, with arable land representing less than one-third of agricultural land (about 10 percent of the world's land area). Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for: understanding the structure of a country's agricultural sector; making economic plans and policies for food security; deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used.\n\n### Limitations and exceptions:\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage.\n\n### Other notes:\nAreas of former states are included in the successor states.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.AGRI.ZS (https://data.worldbank.org/indicator/AG.LND.AGRI.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.AGRI.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of land area", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of land area" } }, "ag_lnd_arbl_ha": { "title": "Arable land (hectares)", "description_from_producer": "Arable land (in hectares) includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.\n\n### Statistical concept and methodology:\nMethodology: Temporary fallow land refers to land left fallow for less than five years. The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable.\n\n### Development relevance:\nAgricultural land covers more than one-third of the world's land area. Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n### Limitations and exceptions:\nThe Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. Data on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTrue comparability of the data is limited, however, by variations in definitions, statistical methods, and quality of data. Countries use different definitions land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined.\n\n### Other notes:\nAreas of former states are included in the successor states.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.ARBL.HA (https://data.worldbank.org/indicator/AG.LND.ARBL.HA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.ARBL.HA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "hectares", "short_unit": "hectares", "display": { "name": "Arable land", "shortUnit": "hectares", "unit": "hectares" } }, "ag_lnd_arbl_ha_pc": { "title": "Arable land (hectares per person)", "description_from_producer": "Arable land (hectares per person) includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Temporary fallow land refers to land left fallow for less than five years. The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable. The data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n### Development relevance:\nAgricultural land covers about one-third of the world's land area, with arable land representing less than one-third of agricultural land (about 10 percent of the world's land area). Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n### Limitations and exceptions:\nThe Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTrue comparability of the data is limited, by variations in definitions, statistical methods, and quality of data. Countries use different definitions of land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined.\n\n### Other notes:\nAreas of former states are included in the successor states.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.ARBL.HA.PC (https://data.worldbank.org/indicator/AG.LND.ARBL.HA.PC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.ARBL.HA.PC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "hectares per person", "short_unit": "hectares", "display": { "shortUnit": "hectares", "unit": "hectares per person" } }, "ag_lnd_arbl_zs": { "title": "Arable land (% of land area)", "description_from_producer": "Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Temporary fallow land refers to land left fallow for less than five years. The abandoned land resulting from shifting cultivation is not included in this category. Data for \"Arable land\" are not meant to indicate the amount of land that is potentially cultivable. Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. The data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n### Development relevance:\nAgricultural land covers more than one-third of the world's land area. Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO's agricultural land data contains a wide range of information on variables that are significant for: understanding the structure of a country's agricultural sector; making economic plans and policies for food security; deriving environmental indicators, including those related to investment in agriculture and data on gross crop area and net crop area which are useful for policy formulation and monitoring.\n\n### Limitations and exceptions:\nThe Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\nThe data collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. Data on agricultural land are valuable for conducting studies on various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\nTrue comparability of the data is limited, by variations in definitions, statistical methods, and quality of data. Countries use different definitions of land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined.\n\n### Other notes:\nAreas of former states are included in the successor states.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and website, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.ARBL.ZS (https://data.worldbank.org/indicator/AG.LND.ARBL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.ARBL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of land area", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of land area" } }, "ag_lnd_crel_ha": { "title": "Land under cereal production (hectares)", "description_from_producer": "Land under cereal production refers to harvested area, although some countries report only sown or cultivated area. Cereals include wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Cereals production includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA cereal is a grass cultivated for the edible components of their grain, composed of the endosperm, germ, and bran. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop; cereal crops therefore can also be called staple crops.\n\n### Development relevance:\nThe cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn developed countries, cereal crops are universally machine-harvested, typically using a combine harvester, which cuts, threshes, and winnows the grain during a single pass across the field. In many industrialized countries, particularly in the United States and Canada, farmers commonly deliver their newly harvested grain to a grain elevator or a storage facility that consolidates the crops of many farmers. In developing countries, a variety of harvesting methods are used in cereal cultivation, depending on the cost of labor, from small combines to hand tools such as the scythe or cradle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production systems have evolved rapidly over the past century and have resulted in significantly increased crop yields, but have also created undesirable environmental side-effects such as soil degradation and erosion, pollution from chemical fertilizers and agrochemicals and a loss of bio-diversity. Factors such as the green revolution, has led to impressive progress in increasing cereals yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used.\n\n### Limitations and exceptions:\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. They are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses.\n\n### Other notes:\nThe world and regional aggregate series do not include data from countries that no longer exist.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/QCL, note: Item code F1717, publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.CREL.HA (https://data.worldbank.org/indicator/AG.LND.CREL.HA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.CREL.HA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "hectares", "short_unit": "hectares", "display": { "shortUnit": "hectares", "unit": "hectares" } }, "ag_lnd_crop_zs": { "title": "Permanent cropland (% of land area)", "description_from_producer": "Permanent cropland is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data on Permanent cropland and land area are collected by the Food and Agriculture Organization (FAO) of the United Nations from official national sources through a questionnaire and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n### Development relevance:\nAgricultural land covers more than one-third of the world's land area. Agricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrops are divided into temporary and permanent crops. Permanent crops are sown or planted once, and then occupy the land for some years and need not be replanted after each annual harvest, such as cocoa, coffee and rubber. This category includes flowering shrubs, fruit trees, nut trees and vines, but excludes trees grown for wood or timber. Temporary crops are those which are both sown and harvested during the same agricultural year, sometimes more than once. Temporary crop land is used for crops with a less than one-year growing cycle and which must be newly sown or planted for further production after the harvest.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Excessive use of chemical fertilizers can alter the chemistry of soil. Pesticide poisoning is common in developing countries. And salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is significant geographic variation in the availability of land considered suitable for agriculture. Increasing population and demand from other sectors place growing pressure on available resources. According to FAO, the world's cultivated area has grown by 12 percent over the last 50 years. The global irrigated area has doubled over the same period, accounting for most of the net increase in cultivated land. Agriculture already uses 11 percent of the world's land surface for crop production. It also makes use of 70 percent of all water withdrawn from aquifers, streams and lakes. Agricultural policies have primarily benefitted farmers with productive land and access to water, bypassing the majority of small-scale producers who are still locked in a poverty trap of high vulnerability, land degradation and climatic uncertainty.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLand resources are central to agriculture and rural development, and are intrinsically linked to global challenges of food insecurity and poverty, climate change adaptation and mitigation, as well as degradation and depletion of natural resources that affect the livelihoods of millions of rural people across the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land.\n\n### Limitations and exceptions:\nThe Food and Agriculture Organization (FAO) tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\nTrue comparability of the data is limited by variations in definitions, statistical methods, and quality of data. Countries use different definitions of land use. The Food and Agriculture Organization of the United Nations (FAO), the primary compiler of the data, occasionally adjusts its definitions of land use categories and revises earlier data. Because the data reflect changes in reporting procedures as well as actual changes in land use, apparent trends should be interpreted cautiously.\n\nSatellite images show land use that differs from that of ground-based measures in area under cultivation and type of land use. Moreover, land use data in some countries (India is an example) are based on reporting systems designed for collecting tax revenue. With land taxes no longer a major source of government revenue, the quality and coverage of land use data have declined.\n\n### Other notes:\nAreas of former states are included in the successor states.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.CROP.ZS (https://data.worldbank.org/indicator/AG.LND.CROP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.CROP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of land area", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of land area" } }, "ag_lnd_el5m_ru_k2": { "title": "Rural land area where elevation is below 5 meters (sq. km)", "description_from_producer": "Rural land area below 5m is the total rural land area in square kilometers where the elevation is 5 meters or less.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA\u2019s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):\n\n### Development relevance:\nScientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems.\n\n### Limitations and exceptions:\nThe 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is \u201cunequivocal\u201d and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20\u201386 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5\u00b0C by 2100 (Kattenberg and others 1996) and increase of \u201ckiller\u201d heat waves during the warm season (Karl and others 1997).", "origins": [ { "producer": "NASA Socioeconomic Data and Applications Center (SEDAC) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021. Indicator AG.LND.EL5M.RU.K2 (https://data.worldbank.org/indicator/AG.LND.EL5M.RU.K2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.EL5M.RU.K2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "sq. km", "short_unit": "sq. km", "display": { "shortUnit": "sq. km", "unit": "sq. km" } }, "ag_lnd_el5m_ru_zs": { "title": "Rural land area where elevation is below 5 meters (% of total land area)", "description_from_producer": "Rural land area below 5m is the percentage of total land where the rural land elevation is 5 meters or less.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA\u2019s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):\n\n### Development relevance:\nScientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems.\n\n### Limitations and exceptions:\nThe 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is \u201cunequivocal\u201d and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20\u201386 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5\u00b0C by 2100 (Kattenberg and others 1996) and increase of \u201ckiller\u201d heat waves during the warm season (Karl and others 1997).", "origins": [ { "producer": "NASA Socioeconomic Data and Applications Center (SEDAC) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021. Indicator AG.LND.EL5M.RU.ZS (https://data.worldbank.org/indicator/AG.LND.EL5M.RU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.EL5M.RU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total land area", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total land area" } }, "ag_lnd_el5m_ur_k2": { "title": "Urban land area where elevation is below 5 meters (sq. km)", "description_from_producer": "Urban land area below 5m is the total urban land area in square kilometers where the elevation is 5 meters or less.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA\u2019s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):\n\n### Development relevance:\nScientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems.", "origins": [ { "producer": "NASA Socioeconomic Data and Applications Center (SEDAC) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021. Indicator AG.LND.EL5M.UR.K2 (https://data.worldbank.org/indicator/AG.LND.EL5M.UR.K2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.EL5M.UR.K2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "sq. km", "short_unit": "sq. km", "display": { "shortUnit": "sq. km", "unit": "sq. km" } }, "ag_lnd_el5m_ur_zs": { "title": "Urban land area where elevation is below 5 meters (% of total land area)", "description_from_producer": "Urban land area below 5m is the percentage of total land where the urban land elevation is 5 meters or less.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA\u2019s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):\n\n### Development relevance:\nScientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems.", "origins": [ { "producer": "NASA Socioeconomic Data and Applications Center (SEDAC) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021. Indicator AG.LND.EL5M.UR.ZS (https://data.worldbank.org/indicator/AG.LND.EL5M.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.EL5M.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total land area", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total land area" } }, "ag_lnd_el5m_zs": { "title": "Land area where elevation is below 5 meters (% of total land area)", "description_from_producer": "Land area below 5m is the percentage of total land where the elevation is 5 meters or less.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Elevation data used to generate the low elevation coastal zones come from the SRTM3 Enhanced Global Map developed by ISCIENCES. The ISCIENCES digital elevation model was created using NASA\u2019s Jet Propulsion Laboratory Shuttle Radar Topography Mission data processed to 3 arc-seconds (SRTM3).\nStatistical concept(s):\n\n### Development relevance:\nScientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems.\n\n### Limitations and exceptions:\nThe 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is \u201cunequivocal\u201d and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20\u201386 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5\u00b0C by 2100 (Kattenberg and others 1996) and increase of \u201ckiller\u201d heat waves during the warm season (Karl and others 1997).", "origins": [ { "producer": "NASA Socioeconomic Data and Applications Center (SEDAC) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev3-3.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2021. Indicator AG.LND.EL5M.ZS (https://data.worldbank.org/indicator/AG.LND.EL5M.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.EL5M.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total land area", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total land area" } }, "ag_lnd_frst_k2": { "title": "Forest area (sq. km)", "description_from_producer": "Forest area is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO provides detail information on forest cover, and adjusted estimates of forest cover. The current survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, this indicator data does not reflect that breakdown. Thus the deforestation data may underestimate the rate at which natural forest is disappearing in some countries.\nStatistical concept(s): Forest - Forests are lands of more than 0.5 hectares, with a tree canopy cover of more than 10 percent, which are not primarily under agricultural or urban land use. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation which have yet to reach a crown density of 10 percent or tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, that are expected to regenerate. The term specifically includes: forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks and shelterbelts of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry purposes, including rubberwood plantations and cork oak stands. The term specifically excludes trees planted primarily for agricultural production, for example in fruit plantations and agroforestry systems.\n\n### Development relevance:\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Also, about 80 percent of the world's forests are publicly owned, but ownership and management of forests by communities, individuals and private companies is on the rise.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClose to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes.\n\n### Limitations and exceptions:\nThe Food and Agricultural Organization (FAO) has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends.\n\n### Other notes:\nThe world and regional aggregate series do not include data from countries that no longer exist.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.FRST.K2 (https://data.worldbank.org/indicator/AG.LND.FRST.K2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.FRST.K2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "sq. km", "short_unit": "sq. km", "display": { "shortUnit": "sq. km", "unit": "sq. km" } }, "ag_lnd_frst_zs": { "title": "Forest area (% of land area)", "description_from_producer": "Forest area (% of land area) is the share of total land area that is under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Forest is determined both by the presence of trees and the absence of other predominant land uses. The trees should reach a minimum height of 5 meters in situ. Areas under reforestation that have not yet reached but are expected to reach a canopy cover of 10 percent and a tree height of 5 meters are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, which are expected to regenerate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Food and Agriculture Organization (FAO) provides detail information on forest cover, and adjusted estimates of forest cover. The survey uses a uniform definition of forest. Although FAO provides a breakdown of forest cover between natural forest and plantation for developing countries, forest data used to derive this indicator data does not reflect that breakdown. Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe indicator is derived by dividing total area under forest of a country by country's total land area, and multiplying by 100.\nStatistical concept(s): Forest - Forests are lands of more than 0.5 hectares, with a tree canopy cover of more than 10 percent, which are not primarily under agricultural or urban land use. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters in situ. Areas under reforestation which have yet to reach a crown density of 10 percent or tree height of 5 m are included, as are temporarily unstocked areas, resulting from human intervention or natural causes, that are expected to regenerate. The term specifically includes: forest nurseries and seed orchards that constitute an integral part of the forest; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks and shelterbelts of trees with an area of more than 0.5 ha and width of more than 20 m; plantations primarily used for forestry purposes, including rubberwood plantations and cork oak stands. The term specifically excludes trees planted primarily for agricultural production, for example in fruit plantations and agroforestry systems.\n\n### Development relevance:\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOn a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems. The decrease of forest area, .11 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDestruction of rainforests remains a significant environmental problem Much of what remains of the world's rainforests is in the Amazon basin, where the Amazon Rainforest covers approximately 4 million square kilometers. The regions with the highest tropical deforestation rate are in Central America and tropical Asia. FAO estimates that the decrease of primary forest area, 0.4 percent over a ten-year period, is largely due to reclassification of primary forest to \"other naturally regenerated forest\" because of selective logging and other human interventions. Large-scale planting of trees is significantly reducing the net loss of forest area globally, and afforestation and natural expansion of forests in some countries and regions have reduced the net loss of forest area significantly at the global level.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nForests cover about 31 percent of total land area of the world; the world's total forest area is just over 4 billion hectares. On a global average, more than one-third of all forest is primary forest, i.e. forest of native species where there are no clearly visible indications of human activities and the ecological processes have not been significantly disturbed. Primary forests, in particular tropical moist forests, include the most species-rich, diverse terrestrial ecosystems.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNational parks, game reserves, wilderness areas and other legally established protected areas cover more than 10 percent of the total forest area in most countries and regions. FAO estimates that around 10 million people are employed in forest management and conservation - but many more are directly dependent on forests for their livelihoods. Close to 1.2 billion hectares of forest are managed primarily for the production of wood and non-wood forest products. An additional 25 percent of forest area is designated for multiple uses - in most cases including the production of wood and non-wood forest products. The area designated primarily for productive purposes has decreased by more than 50 million hectares since 1990 as forests have been designated for other purposes.\n\n### Limitations and exceptions:\nFAO has been collecting and analyzing data on forest area since 1946. This is done at intervals of 5-10 years as part of the Global Forest Resources Assessment (FRA). FAO reports data for 229 countries and territories; for the remaining 56 small island states and territories where no information is provided, a report is prepared by FAO using existing information and a literature search. The data are aggregated at sub-regional, regional and global levels by the FRA team at FAO, and estimates are produced by straight summation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe lag between the reference year and the actual production of data series as well as the frequency of data production varies between countries. Deforested areas do not include areas logged but intended for regeneration or areas degraded by fuelwood gathering, acid precipitation, or forest fires. Negative numbers indicate an increase in forest area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData includes areas with bamboo and palms; forest roads, firebreaks and other small open areas; forest in national parks, nature reserves and other protected areas such as those of specific scientific, historical, cultural or spiritual interest; windbreaks, shelterbelts and corridors of trees with an area of more than 0.5 hectares and width of more than 20 meters; plantations primarily used for forestry or protective purposes, such as rubber-wood plantations and cork oak stands. Data excludes tree stands in agricultural production systems, such as fruit plantations and agroforestry systems. Forest area also excludes trees in urban parks and gardens. The proportion of forest area to total land area is calculated and changes in the proportion are computed to identify trends.\n\n### Other notes:\nThe world and regional aggregate series do not include data from countries that no longer exist.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.FRST.ZS (https://data.worldbank.org/indicator/AG.LND.FRST.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.FRST.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of land area", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of land area" } }, "ag_lnd_irig_ag_zs": { "title": "Agricultural irrigated land (% of total agricultural land)", "description_from_producer": "Agricultural irrigated land refers to agricultural areas purposely provided with water, including land irrigated by controlled flooding.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Irrigated agricultural area refers to area equipped to provide water (via artificial means of irrigation such as by diverting streams, flooding, or spraying) to the crops. In non-irrigated agricultural areas, production of crops is dependent on rain-fed irrigation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural land constitutes only a part of any country's total area, which can include areas not suitable for agriculture, such as forests, mountains, and inland water bodies. Agricultural land can also be classified as irrigated and non-irrigated land. In arid and semi-arid countries agriculture is often confined to irrigated land, with very little farming possible in non-irrigated areas.\n\n### Development relevance:\nWorldwide, irrigated agriculture accounts for about four-fifths of global water withdrawals. The share of irrigated land ranges widely, from 4 percent of the total area cropped in Africa to 42 percent in South Asia. The leading countries are India and China with about 30 percent and 52 percent of all cropland irrigated, respectively. Without irrigation and drainage, much of the increases in agricultural output that has fed the world's growing population and stabilized food production would not have been possible.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn the dry sub-humid countries, irrigation is critical for crop production. Due to highly variable rainfall, long dry seasons, and recurrent droughts, dry spells and floods, water management is a key determinant for agricultural production in these regions and is increasingly becoming more important with climate change. World Bank estimates that rainfed agriculture is most significant in Sub-Saharan Africa where it accounts for about 96 percent of the cropland.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIrrigation and drainage continue to be an important source of productivity growth, especially in Sub-Saharan Africa and parts of Latin America that still have large untapped water resources for agriculture. In other regions where the scope for further expanding irrigated agriculture is limited, more efforts are needed to enhance the policy, technical, and governance aspects of agricultural water use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural land covers more than one-third of the world's land area. In many industrialized countries, agricultural land is subject to zoning regulations. In the context of zoning, agricultural land (or more properly agriculturally zoned land) refers to plots that may be used for agricultural activities, regardless of the physical type or quality of land. Data on agricultural land are valuable for conducting studies on a various perspectives concerning agricultural production, food security and for deriving cropping intensity among others uses. Agricultural land indicator, along with land-use indicators, can also elucidate the environmental sustainability of countries' agricultural practices.\n\n\n\n\n\n\n\n\nAgriculture is still a major sector in many economies, and agricultural activities provide developing countries with food and revenue. But agricultural activities also can degrade natural resources. Poor farming practices can cause soil erosion and loss of soil fertility. Efforts to increase productivity by using chemical fertilizers, pesticides, and intensive irrigation have environmental costs and health impacts. Salinization of irrigated land diminishes soil fertility. Thus, inappropriate use of inputs for agricultural production has far-reaching effects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is no single correct mix of inputs to the agricultural land, as it is dependent on local climate, land quality, and economic development; appropriate levels and application rates vary by country and over time and depend on the type of crops, the climate and soils, and the production process used.\n\n### Limitations and exceptions:\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n### Other notes:\nAreas of former states are included in the successor states.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.IRIG.AG.ZS (https://data.worldbank.org/indicator/AG.LND.IRIG.AG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.IRIG.AG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total agricultural land", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total agricultural land" } }, "ag_lnd_prcp_mm": { "title": "Average precipitation in depth (mm per year)", "description_from_producer": "Average precipitation is the long-term average in depth (over space and time) of annual precipitation in the country. Precipitation is defined as any kind of water that falls from clouds as a liquid or a solid.\n\n### Statistical concept and methodology:\nMethodology: Estimation of Areal Precipitation\n\n\n\n\n\nA single point precipitation measurement is quite often not representative of the volume of precipitation falling over a given catchment area. A dense network of point measurements and/or radar estimates can provide a better representation of the true volume over a given area. A network of precipitation measurements is converted to areal estimates using the arithmetic mean. This technique calculates areal precipitation using the arithmetic mean of all the point or areal measurements considered in the analysis.\nStatistical concept(s):\n\n### Development relevance:\nThe agriculture sector is the most water-intensive sector, and water delivery in agriculture is increasingly important. Data on irrigated agricultural land and data on average precipitation illustrate how countries obtain water for agricultural use.\n\n### Limitations and exceptions:\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.PRCP.MM (https://data.worldbank.org/indicator/AG.LND.PRCP.MM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.PRCP.MM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "mm per year", "short_unit": "mm", "display": { "shortUnit": "mm", "unit": "mm per year" } }, "ag_lnd_totl_k2": { "title": "Land area (sq. km)", "description_from_producer": "Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Total land area does not include inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. Including areas of former states; for example, the areas of the Union of Soviet Socialist Republics (USSR) are counted in Russian Federation and other successor states.\n\n### Development relevance:\nLand area is particularly important for understanding an economy's agricultural capacity and the environmental effects of human activity. Innovations in satellite mapping and computer databases have resulted in more precise measurements of land and water areas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation, land area, income, and output are basic measures of the size of an economy. They also provide a broad indication of actual and potential resources. Land area is therefore used as one of the major indicators to normalize other indicators.\n\n### Limitations and exceptions:\nThe data are collected by the Food and Agriculture Organization (FAO) of the United Nations through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n### Other notes:\nAreas of former states are included in the successor states.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAOSTAT, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/RL, publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.LND.TOTL.K2 (https://data.worldbank.org/indicator/AG.LND.TOTL.K2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.TOTL.K2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "sq. km", "short_unit": "sq. km", "display": { "shortUnit": "sq. km", "unit": "sq. km" } }, "ag_lnd_totl_ru_k2": { "title": "Rural land area (sq. km)", "description_from_producer": "Rural land area in square kilometers, derived from urban extent grids which distinguish urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) urban extent grid distinguishes urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights . Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT)\nStatistical concept(s):\n\n### Limitations and exceptions:\nThe 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is \u201cunequivocal\u201d and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20\u201386 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5\u00b0C by 2100 (Kattenberg and others 1996) and increase of \u201ckiller\u201d heat waves during the warm season (Karl and others 1997).", "origins": [ { "producer": "Center for International Earth Science Information Network (CIESIN) - NASA SEDAC Low Elevation Coastal Zone (LECZ), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev2-2.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2013. Indicator AG.LND.TOTL.RU.K2 (https://data.worldbank.org/indicator/AG.LND.TOTL.RU.K2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.TOTL.RU.K2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "sq. km", "short_unit": "sq. km", "display": { "shortUnit": "sq. km", "unit": "sq. km" } }, "ag_lnd_totl_ur_k2": { "title": "Urban land area (sq. km)", "description_from_producer": "Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of nighttime lights. Areas are defined as urban where contiguous lighted cells from the nighttime lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) urban extent grid distinguishes urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights . Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT)", "origins": [ { "producer": "Center for International Earth Science Information Network (CIESIN) - NASA SEDAC Low Elevation Coastal Zone (LECZ), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 2, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://www.earthdata.nasa.gov/data/catalog/sedac-ciesin-sedac-lecz-urplaev2-2.00, publisher: NASA Socioeconomic Data and Applications Center (SEDAC), date published: 2013. Indicator AG.LND.TOTL.UR.K2 (https://data.worldbank.org/indicator/AG.LND.TOTL.UR.K2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.LND.TOTL.UR.K2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "sq. km", "short_unit": "sq. km", "display": { "shortUnit": "sq. km", "unit": "sq. km" } }, "ag_prd_crel_mt": { "title": "Cereal production (metric tons)", "description_from_producer": "Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: A cereal is a grass cultivated for the edible components of their grain, composed of the endosperm, germ, and bran. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop; cereal crops therefore can also be called staple crops. Cereals production data relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The Food and Agriculture Organization (FAO) allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year.\n\n### Development relevance:\nThe Food and Agriculture Organization (FAO) estimates that cereals supply 51 percent of Calories and 47 percent of protein in the average diet. The total annual cereal production globally is about 2,500 million tons.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFAO estimates that maize (corn), wheat and rice together account for more than three-fourths of all grain production worldwide. In developed countries, cereal crops are universally machine-harvested, typically using a combine harvester, which cuts, threshes, and winnows the grain during a single pass across the field. In many industrialized countries, particularly in the United States and Canada, farmers commonly deliver their newly harvested grain to a grain elevator or a storage facility that consolidates the crops of many farmers. In developing countries, a variety of harvesting methods are used in cereal cultivation, depending on the cost of labor, from small combines to hand tools such as the scythe or cradle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production systems have evolved rapidly over the past century and have resulted in significantly increased crop yields, but have also created undesirable environmental side-effects such as soil degradation and erosion, pollution from chemical fertilizers and agrochemicals and a loss of bio-diversity. Factors such as the green revolution, has led to impressive progress in increasing cereals yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\n### Limitations and exceptions:\nData on cereal production may be affected by a variety of reporting and timing differences. Millet and sorghum, which are grown as feed for livestock and poultry in Europe and North America, are used as food in Africa, Asia, and countries of the former Soviet Union. So some cereal crops are excluded from the data for some countries and included elsewhere, depending on their use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization (FAO) of the United Nations through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources.\n\n### Other notes:\nThe world and regional aggregate series do not include data from countries that no longer exist.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/QCL, publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.PRD.CREL.MT (https://data.worldbank.org/indicator/AG.PRD.CREL.MT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.PRD.CREL.MT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "metric tons", "display": { "unit": "metric tons" } }, "ag_prd_crop_xd": { "title": "Crop production index (2014-2016 = 100)", "description_from_producer": "Crop production index shows agricultural production for each year relative to the base period 2014-2016. It includes all crops except fodder crops. Regional and income group aggregates for the FAO's production indexes are calculated from the underlying values in international dollars, normalized to the base period 2014-2016.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The agricultural production index is prepared by the Food and Agriculture Organization of the United Nations (FAO). The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2014-2016. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula*. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross\". The \"international commodity prices\" are used in order to avoid the use of exchange rates for obtaining continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These\" international prices,\" expressed in so-called \"international dollars,\" are derived using a Geary-Khamis formula** for the agricultural sector. This method assigns a single \"price\" to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops.\n* A Laspeyres Index is known as a \"base-weighted\" or \"fixed-weighted\" index because the price increases are weighted by the quantities in the base period. The Consumer Price Index is an example of a Laspeyres Index. http://www.usna.edu/Users/econ/rbrady/312%20Materials/LaspeyresCalc.pdf\n** Geary-Khamis formula is an aggregation method in which category \"international prices\" (reflecting relative category values) and country purchasing power parities (PPPs), (depicting relative country price levels) are estimated simultaneously from a system of linear equations. http://stats.oecd.org/glossary/detail.asp?ID=5528\n\n### Development relevance:\nThe commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. The category of food production includes commodities that are considered edible and that contain nutrients. Accordingly, coffee and tea are excluded along with inedible commodities because, although edible, they have practically no nutritive value.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption).\n\n### Limitations and exceptions:\nThe FAO indices may differ from those produced by the countries themselves because of differences in concepts of production, coverage, time periods, weights, time reference of data, methods of calculation, and use of international prices.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAgricultural data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through annual questionnaires and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.PRD.CROP.XD (https://data.worldbank.org/indicator/AG.PRD.CROP.XD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.PRD.CROP.XD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2014-2014 = 100", "display": { "unit": "2014-2014 = 100" } }, "ag_prd_food_xd": { "title": "Food production index (2014-2016 = 100)", "description_from_producer": "Food production index covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive value.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The agricultural production index is prepared by the Food and Agriculture Organization of the United Nations (FAO). The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2014-2016. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula*. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross\". The \"international commodity prices\" are used in order to avoid the use of exchange rates for obtaining continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These\" international prices,\" expressed in so-called \"international dollars,\" are derived using a Geary-Khamis formula** for the agricultural sector. This method assigns a single \"price\" to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops.\n* A Laspeyres Index is known as a \"base-weighted\" or \"fixed-weighted\" index because the price increases are weighted by the quantities in the base period. The Consumer Price Index is an example of a Laspeyres Index. http://www.usna.edu/Users/econ/rbrady/312%20Materials/LaspeyresCalc.pdf\n** Geary-Khamis formula is an aggregation method in which category \"international prices\" (reflecting relative category values) and country purchasing power parities (PPPs), (depicting relative country price levels) are estimated simultaneously from a system of linear equations. http://stats.oecd.org/glossary/detail.asp?ID=5528\n\n### Development relevance:\nThe commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. The category of food production includes commodities that are considered edible and that contain nutrients. Accordingly, coffee and tea are excluded along with inedible commodities because, although edible, they have practically no nutritive value.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption).\n\n### Limitations and exceptions:\nAgricultural data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through the questionnaire and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.PRD.FOOD.XD (https://data.worldbank.org/indicator/AG.PRD.FOOD.XD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.PRD.FOOD.XD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2014-2014 = 100", "display": { "unit": "2014-2014 = 100" } }, "ag_prd_lvsk_xd": { "title": "Livestock production index (2014-2016 = 100)", "description_from_producer": "Livestock production index includes meat and milk from all sources, dairy products such as cheese, and eggs, honey, raw silk, wool, and hides and skins.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The agricultural production index is prepared by the Food and Agriculture Organization of the United Nations (FAO). The FAO indices of agricultural production show the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2014-2016. They are based on the sum of price-weighted quantities of different agricultural commodities produced after deductions of quantities used as seed and feed weighted in a similar manner. The resulting aggregate represents, therefore, disposable production for any use except as seed and feed. All the indices at the country, regional and world levels are calculated by the Laspeyres formula*. Production quantities of each commodity are weighted by 2014-2016 average international commodity prices and summed for each year. To obtain the index, the aggregate for a given year is divided by the average aggregate for the base period 2014-2016. Since the FAO indices are based on the concept of agriculture as a single enterprise, amounts of seed and feed are subtracted from the production data to avoid double counting, once in the production data and once with the crops or livestock produced from them. Deductions for seed (in the case of eggs, for hatching) and for livestock and poultry feed apply to both domestically produced and imported commodities. They cover only primary agricultural products destined to animal feed (e.g. maize, potatoes, milk, etc.). Processed and semi-processed feed items such as bran, oilcakes, meals and molasses have been completely excluded from the calculations at all stages. It should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed. Indices which take into account deductions for feed and seed are referred to as ''net''. Indices calculated without any deductions for feed and seed are referred to as ''gross\". The \"international commodity prices\" are used in order to avoid the use of exchange rates for obtaining continental and world aggregates, and also to improve and facilitate international comparative analysis of productivity at the national level. These\" international prices,\" expressed in so-called \"international dollars,\" are derived using a Geary-Khamis formula** for the agricultural sector. This method assigns a single \"price\" to each commodity. For example, one metric ton of wheat has the same price regardless of the country where it was produced. The currency unit in which the prices are expressed has no influence on the indices published. The commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops.\n* A Laspeyres Index is known as a \"base-weighted\" or \"fixed-weighted\" index because the price increases are weighted by the quantities in the base period. The Consumer Price Index is an example of a Laspeyres Index. http://www.usna.edu/Users/econ/rbrady/312%20Materials/LaspeyresCalc.pdf\n** Geary-Khamis formula is an aggregation method in which category \"international prices\" (reflecting relative category values) and country purchasing power parities (PPPs), (depicting relative country price levels) are estimated simultaneously from a system of linear equations. http://stats.oecd.org/glossary/detail.asp?ID=5528\n\n### Development relevance:\nThe commodities covered in the computation of indices of agricultural production are all crops and livestock products originating in each country. Practically all products are covered, with the main exception of fodder crops. The category of food production includes commodities that are considered edible and that contain nutrients. Accordingly, coffee and tea are excluded along with inedible commodities because, although edible, they have practically no nutritive value.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt should be noted that when calculating indices of agricultural, food and nonfood production, all intermediate primary inputs of agricultural origin are deducted. However, for indices of any other commodity group, only inputs originating from within the same group are deducted; thus, only seed is removed from the group \"crops\" and from all crop subgroups, such as cereals, oil crops, etc.; and both feed and seed originating from within the livestock sector (e.g. milk feed, hatching eggs) are removed from the group \"livestock products\". For the main two livestock subgroups, namely, meat and milk, only feed originating from the respective subgroup is removed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production data refer to the actual harvested production from the field or orchard and gardens, excluding harvesting and threshing losses and that part of crop not harvested for any reason. Production therefore includes the quantities of the commodity sold in the market (marketed production) and the quantities consumed or used by the producers (auto-consumption).\n\n### Limitations and exceptions:\nAgricultural data are collected by the Food and Agriculture Organization of the United Nations (FAO) from official national sources through the questionnaire and are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Data on agricultural employment, in particular, should be used with caution. In many countries much agricultural employment is informal and unrecorded, including substantial work performed by women and children. To address some of these concerns, this indicator is heavily footnoted in the database in sources, definition, and coverage.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.PRD.LVSK.XD (https://data.worldbank.org/indicator/AG.PRD.LVSK.XD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.PRD.LVSK.XD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2014-2014 = 100", "display": { "unit": "2014-2014 = 100" } }, "ag_srf_totl_k2": { "title": "Surface area (sq. km)", "description_from_producer": "Surface area is a country's total area, including areas under inland bodies of water and some coastal waterways.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Total land area includes inland water bodies such as major rivers and lakes. Variations from year to year may be due to updated or revised data rather than to change in area. Including areas of former states; for example, the areas of the Union of Soviet Socialist Republics (USSR) are counted in Russian Federationand other successor states.\n\n### Development relevance:\nTotal surface area is particularly important for understanding an economy's agricultural capacity and the environmental effects of human activity. Innovations in satellite mapping and computer databases have resulted in more precise measurements of land and water areas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation, surface area, income, and output are basic measures of the size of an economy. They also provide a broad indication of actual and potential resources. Land area is therefore used as one of the major indicator to normalize other indicators.\n\n### Limitations and exceptions:\nThe data are collected by the Food and Agriculture Organization (FAO) of the United Nations through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n### Other notes:\nAreas of former states are included in the successor states.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.SRF.TOTL.K2 (https://data.worldbank.org/indicator/AG.SRF.TOTL.K2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.SRF.TOTL.K2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "sq. km", "short_unit": "sq. km", "display": { "shortUnit": "sq. km", "unit": "sq. km" } }, "ag_yld_crel_kg": { "title": "Cereal yield (kg per hectare)", "description_from_producer": "Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: A cereal is a grass cultivated for the edible components of their grain, composed of the endosperm, germ, and bran. Cereal yield is measured as kilograms per hectare of harvested land. Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop; cereal crops therefore can also be called staple crops Cereals production includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded.\n\n### Development relevance:\nIn developed countries, cereal crops are universally machine-harvested, typically using a combine harvester, which cuts, threshes, and winnows the grain during a single pass across the field. In many industrialized countries, particularly in the United States and Canada, farmers commonly deliver their newly harvested grain to a grain elevator or a storage facility that consolidates the crops of many farmers. In developing countries, a variety of harvesting methods are used in cereal cultivation, depending on the cost of labor, from small combines to hand tools such as the scythe or cradle.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCrop production systems have evolved rapidly over the past century and have resulted in significantly increased crop yields, but have also created undesirable environmental side-effects such as soil degradation and erosion, pollution from chemical fertilizers and agrochemicals and a loss of bio-diversity. Factors such as the green revolution have led to impressive progress in increasing cereals yields over the last few decades. This progress, however, is not equal across all regions. Continued progress depends on maintaining agricultural research and education. The cultivation of cereals varies widely in different countries and depends partly upon the development of the economy. Production depends on the nature of the soil, the amount of rainfall, irrigation, quality of seeds, and the techniques applied to promote growth.\n\n### Limitations and exceptions:\nCereals production data relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are collected by the Food and Agriculture Organization of the United Nations (FAO) through annual questionnaires. The FAO tries to impose standard definitions and reporting methods, but complete consistency across countries and over time is not possible. Thus, data on agricultural land in different climates may not be comparable. For example, permanent pastures are quite different in nature and intensity in African countries and dry Middle Eastern countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on cereal yield may be affected by a variety of reporting and timing differences. Millet and sorghum, which are grown as feed for livestock and poultry in Europe and North America, are used as food in Africa, Asia, and countries of the former Soviet Union. So some cereal crops are excluded from the data for some countries and included elsewhere, depending on their use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data collected from official national sources through the questionnaire are supplemented with information from official secondary data sources. The secondary sources cover official country data from websites of national ministries, national publications and related country data reported by various international organizations.\n\n### Other notes:\nThe world and regional aggregate series do not include data from countries that no longer exist.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO electronic files and web site, Food and Agriculture Organization of the United Nations (FAO), uri: https://www.fao.org/faostat/en/#data/QCL, publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator AG.YLD.CREL.KG (https://data.worldbank.org/indicator/AG.YLD.CREL.KG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/AG.YLD.CREL.KG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "kg per hectare", "short_unit": "kg", "display": { "shortUnit": "kg", "unit": "kg per hectare" } }, "bg_gsr_nfsv_gd_zs": { "title": "Trade in services (% of GDP)", "description_from_producer": "Total trade in services includes services provided by residents to non-residents plus services provided by non-residents to residents. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), OECD, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD). Indicator BG.GSR.NFSV.GD.ZS (https://data.worldbank.org/indicator/BG.GSR.NFSV.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BG.GSR.NFSV.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "bm_gsr_cmcp_zs": { "title": "Communications, computer, etc. (% of service imports, BoP)", "description_from_producer": "Communications, computer, information, and other services cover international telecommunications; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; personal, cultural, and recreational services; manufacturing services on physical inputs owned by others; and maintenance and repair services and government services not included elsewhere. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BM.GSR.CMCP.ZS (https://data.worldbank.org/indicator/BM.GSR.CMCP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.GSR.CMCP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of service imports, BoP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of service imports, BoP" } }, "bm_gsr_fcty_cd": { "title": "Primary income payments (BoP, current US$)", "description_from_producer": "Primary income payments refer to employee compensation paid to nonresident workers and investment income (payments on direct investment, portfolio investment, other investments).This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the earned income account of the balance of payments, which shows amounts payable and receivable in return for providing temporary use to another entity of labor, financial resources, or nonproduced nonfinancial assets. This account can help in understanding the sustainability of a country's current account balance, assessing the country's ability to finance its foreign payments, and analyzing the income generated from foreign assets compared to the payments made to foreign investors. Overall, it provides valuable insights into the flow of income across borders, which is crucial for economic analysis and policymaking.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BM.GSR.FCTY.CD (https://data.worldbank.org/indicator/BM.GSR.FCTY.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.GSR.FCTY.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bm_gsr_gnfs_cd": { "title": "Imports of goods and services (BoP, current US$)", "description_from_producer": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Limitations and exceptions:\nDiscrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BM.GSR.GNFS.CD (https://data.worldbank.org/indicator/BM.GSR.GNFS.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.GSR.GNFS.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bm_gsr_insf_zs": { "title": "Insurance and financial services (% of service imports, BoP)", "description_from_producer": "Insurance and financial services cover various types of insurance provided to nonresidents by resident insurance enterprises and vice versa, and financial intermediary and auxiliary services (except those of insurance enterprises and pension funds) exchanged between residents and nonresidents. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BM.GSR.INSF.ZS (https://data.worldbank.org/indicator/BM.GSR.INSF.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.GSR.INSF.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of service imports, BoP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of service imports, BoP" } }, "bm_gsr_mrch_cd": { "title": "Goods imports (BoP, current US$)", "description_from_producer": "Imports of goods occur when there are changes in the economic ownership of goods from non-residents to residents of the compiling economy, irrespective of physical movement of goods across national borders. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BM.GSR.MRCH.CD (https://data.worldbank.org/indicator/BM.GSR.MRCH.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.GSR.MRCH.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bm_gsr_nfsv_cd": { "title": "Service imports (BoP, current US$)", "description_from_producer": "Imports of services are services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. Manufacturing services on physical inputs owned by others (goods for processing in BPM5) and maintenance and repair services n.i.e. are reclassified from goods to services.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BM.GSR.NFSV.CD (https://data.worldbank.org/indicator/BM.GSR.NFSV.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.GSR.NFSV.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bm_gsr_royl_cd": { "title": "Charges for the use of intellectual property, payments (BoP, current US$)", "description_from_producer": "Charges for the use of proprietary rights (such as patents, trademarks, copyrights, industrial processes and designs including trade secrets, franchises), and charges for licenses to reproduce or distribute (or both) intellectual property embodied in produced originals or prototypes (such as copyrights on books and manuscripts, computer software, cinematographic works, and sound recordings) and related rights (such as for live performances and television, cable, or satellite broadcast). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BM.GSR.ROYL.CD (https://data.worldbank.org/indicator/BM.GSR.ROYL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.GSR.ROYL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bm_gsr_totl_cd": { "title": "Imports of goods, services and primary income (BoP, current US$)", "description_from_producer": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. Primary income represents the return that accrues to institutional units for their contribution to the production process or for the provision of financial assets and renting natural resources to other institutional units. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BM.GSR.TOTL.CD (https://data.worldbank.org/indicator/BM.GSR.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.GSR.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bm_gsr_tran_zs": { "title": "Transport services (% of service imports, BoP)", "description_from_producer": "Transport services covers the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BM.GSR.TRAN.ZS (https://data.worldbank.org/indicator/BM.GSR.TRAN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.GSR.TRAN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of service imports, BoP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of service imports, BoP" } }, "bm_gsr_trvl_zs": { "title": "Travel services (% of service imports, BoP)", "description_from_producer": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service imports which are services provided by non-residents to residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BM.GSR.TRVL.ZS (https://data.worldbank.org/indicator/BM.GSR.TRVL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.GSR.TRVL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of service imports, BoP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of service imports, BoP" } }, "bm_klt_dinv_cd_wd": { "title": "Foreign direct investment, net outflows (BoP, current US$)", "description_from_producer": "Foreign direct investment refers to direct investment equity flows in an economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. This series shows net outflows of investment from the reporting economy to the rest of the world. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments.\n\n### Development relevance:\nPrivate financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits.\n\n### Limitations and exceptions:\nFDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available.\n\n### Other notes:\nNote: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6).", "origins": [ { "producer": "International Monetary Fund (Balance of Payments), UNCTAD, and national sources, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments database, International Monetary Fund (IMF), note: International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources.;\nUN Conference on Trade and Development (UNCTAD);\nOfficial national sources. Indicator BM.KLT.DINV.CD.WD (https://data.worldbank.org/indicator/BM.KLT.DINV.CD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.KLT.DINV.CD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bm_klt_dinv_wd_gd_zs": { "title": "Foreign direct investment, net outflows (% of GDP)", "description_from_producer": "Foreign direct investment refers to direct investment equity flows in an economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. This series shows net outflows of investment from the reporting economy to the rest of the world, and is divided by GDP.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments.\n\n### Development relevance:\nPrivate financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits.\n\n### Limitations and exceptions:\nFDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available.\n\n### Other notes:\nNote: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6).", "origins": [ { "producer": "International Monetary Fund (Balance of Payments), UNCTAD, and national sources, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments database, International Monetary Fund (IMF), note: International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources.;\nUN Conference on Trade and Development (UNCTAD);\nOfficial national sources. Indicator BM.KLT.DINV.WD.GD.ZS (https://data.worldbank.org/indicator/BM.KLT.DINV.WD.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.KLT.DINV.WD.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "bm_trf_prvt_cd": { "title": "Secondary income, other sectors, payments (BoP, current US$)", "description_from_producer": "Secondary income refers to transfers recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BM.TRF.PRVT.CD (https://data.worldbank.org/indicator/BM.TRF.PRVT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.TRF.PRVT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bn_cab_xoka_cd": { "title": "Current account balance (BoP, current US$)", "description_from_producer": "Balance of current transactions (transactions in goods and services, earned income and transfer income) between residents and non-residents. The term current account balance is used in the external accounts and is expressed from the perspective of resident units. The term current external balance is used in the national accounts and is expressed from the perspective of the non-resident units, and therefore with the opposite sign. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the current account of the balance of payments. The current account is a critical component of the balance of payments, tracking a nation\u2019s trade in goods, services, investment earnings, and remittances. It serves as a vital economic health monitor, revealing whether a country is a net lender (surplus) or net borrower (deficit). Ultimately, it signals a nation's global competitiveness, heavily influencing currency strength, foreign investment, and government policy decisions.\n\n### Limitations and exceptions:\nDiscrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BN.CAB.XOKA.CD (https://data.worldbank.org/indicator/BN.CAB.XOKA.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.CAB.XOKA.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bn_cab_xoka_gd_zs": { "title": "Current account balance (% of GDP)", "description_from_producer": "Balance of current transactions (transactions in goods and services, earned income and transfer income) between residents and non-residents. The term current account balance is used in the external accounts and is expressed from the perspective of resident units. The term current external balance is used in the national accounts and is expressed from the perspective of the non-resident units, and therefore with the opposite sign. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the current account of the balance of payments. The current account is a critical component of the balance of payments, tracking a nation\u2019s trade in goods, services, investment earnings, and remittances. It serves as a vital economic health monitor, revealing whether a country is a net lender (surplus) or net borrower (deficit). Ultimately, it signals a nation's global competitiveness, heavily influencing currency strength, foreign investment, and government policy decisions.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), OECD, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD). Indicator BN.CAB.XOKA.GD.ZS (https://data.worldbank.org/indicator/BN.CAB.XOKA.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.CAB.XOKA.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "bn_fin_totl_cd": { "title": "Net financial account (BoP, current US$)", "description_from_producer": "The net financial account shows net acquisition and disposal of financial assets and liabilities. It measures how net lending to or borrowing from nonresidents is financed, and is conceptually equal to the sum of the balances on the current and capital accounts. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the financial account of the balance of payments, which shows net acquisition and disposal of financial assets and liabilities. It is crucial for assessing, among other things (i) capital mobility, reflecting the degree of international capital mobility and the country's integration into the global financial system; (ii) investor confidence and country's creditworthiness; (iii) pressures on a country's currency and central bank's actions in the foreign exchange market; (iv) effectiveness of a country's economic policies, including interest rate and exchange rate policies; or (v) country's vulnerability to external economic shocks and its ability to finance current account deficits. Overall, the financial transactions section is a critical source of information for central banks to formulate and adjust monetary policy to achieve objectives like price stability, full employment, and economic growth.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BN.FIN.TOTL.CD (https://data.worldbank.org/indicator/BN.FIN.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.FIN.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bn_gsr_fcty_cd": { "title": "Net primary income (BoP, current US$)", "description_from_producer": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the earned income account of the balance of payments, which shows amounts payable and receivable in return for providing temporary use to another entity of labor, financial resources, or nonproduced nonfinancial assets. This account can help in understanding the sustainability of a country's current account balance, assessing the country's ability to finance its foreign payments, and analyzing the income generated from foreign assets compared to the payments made to foreign investors. Overall, it provides valuable insights into the flow of income across borders, which is crucial for economic analysis and policymaking.\n\n### Limitations and exceptions:\nDiscrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BN.GSR.FCTY.CD (https://data.worldbank.org/indicator/BN.GSR.FCTY.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.GSR.FCTY.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bn_gsr_gnfs_cd": { "title": "Net trade in goods and services (BoP, current US$)", "description_from_producer": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BN.GSR.GNFS.CD (https://data.worldbank.org/indicator/BN.GSR.GNFS.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.GSR.GNFS.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bn_gsr_mrch_cd": { "title": "Net trade in goods (BoP, current US$)", "description_from_producer": "The balance of international trade in goods is the difference between the exports and imports of goods. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BN.GSR.MRCH.CD (https://data.worldbank.org/indicator/BN.GSR.MRCH.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.GSR.MRCH.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bn_kac_eoms_cd": { "title": "Net errors and omissions (BoP, current US$)", "description_from_producer": "Net errors and omissions constitute a residual category needed to ensure that accounts in the balance of payments statement sum to zero. Net errors and omissions are derived as the balance on the financial account minus the balances on the current and capital accounts. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BN.KAC.EOMS.CD (https://data.worldbank.org/indicator/BN.KAC.EOMS.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.KAC.EOMS.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bn_klt_dinv_cd": { "title": "Foreign direct investment, net (BoP, current US$)", "description_from_producer": "Foreign direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the voting power is evidence of a direct investment relationship. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BN.KLT.DINV.CD (https://data.worldbank.org/indicator/BN.KLT.DINV.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.KLT.DINV.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bn_klt_ptxl_cd": { "title": "Portfolio Investment, net (BoP, current US$)", "description_from_producer": "Portfolio investment includes cross-border flows and positions involving debt or equity securities, other than those included in direct investment or reserve assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BN.KLT.PTXL.CD (https://data.worldbank.org/indicator/BN.KLT.PTXL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.KLT.PTXL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bn_res_incl_cd": { "title": "Reserves and related items (BoP, current US$)", "description_from_producer": "Reserves and related items is the net change in a country's holdings of international reserves resulting from transactions on the current, capital, and financial accounts. Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency.Also included are net credit and loans from the IMF (excluding reserve position) and total exceptional financing. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. In BPM6, the headings of the financial account have been changed from credits and debits to net acquisition of financial assets and net incurrence of liabilities; i.e., all changes due to credit and debit entries are recorded on a net basis separately for financial assets and liabilities. Financial account balances are calculated as the change in assets minus the change in liabilities; signs are reversed from previous editions.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BN.RES.INCL.CD (https://data.worldbank.org/indicator/BN.RES.INCL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.RES.INCL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bn_trf_curr_cd": { "title": "Net secondary income (BoP, current US$)", "description_from_producer": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies.\n\n### Limitations and exceptions:\nDiscrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BN.TRF.CURR.CD (https://data.worldbank.org/indicator/BN.TRF.CURR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.TRF.CURR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bn_trf_kogt_cd": { "title": "Net capital account (BoP, current US$)", "description_from_producer": "Net capital account records acquisitions and disposals of nonproduced nonfinancial assets, such as land sold to embassies and sales of leases and licenses, as well as capital transfers, including government debt forgiveness. The use of the term capital account in this context is designed to be consistent with the System of National Accounts, which distinguishes between capital transactions and financial transactions. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the capital account of the balance of payments. The capital account records acquisitions and disposals of nonproduced nonfinancial assets, such as sales of leases and licenses, crypto assets without a corresponding liability designed as a medium of exchange, as well as capital transfers. These transactions can have a profound impact on a country's economy and are an essential part of understanding the overall balance of payments.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BN.TRF.KOGT.CD (https://data.worldbank.org/indicator/BN.TRF.KOGT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BN.TRF.KOGT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_grt_exta_cd_wd": { "title": "Grants, excluding technical cooperation (BoP, current US$)", "description_from_producer": "Grants are transfers made in cash, goods or services for which no repayment is required. For ODA reporting purposes, they also include forgiveness of non-military debt, support to non-governmental organisations, certain interest subsidies, and certain costs incurred in the implementation of aid. Grants to multilateral agencies intended to soften the terms of the latter\u2019s lending are a direct resource outflow and should also be recorded as ODA grants. For OOF reporting purposes, grants for commercial purposes such as subsidies to national private investors, and grants to forgive military debt, are also included. Grant-like flows are assimilated to grants. They comprise a) loans for which the service payments are to be made into an account in the borrowing country and used in the borrowing country for its own benefit, and b) provision of commodities for sale in the recipient\u2019s currency the proceeds of which are used in the recipient country for its own benefit. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Grants are transfers in cash or in kind for which no legal debt is incurred by the recipient. For ODA reporting purposes, they also include forgiveness of non-military debt, support to non-governmental organisations, certain interest subsidies, and certain costs incurred in the implementation of aid. Grants to multilateral agencies intended to soften the terms of the latter\u2019s lending are a direct resource outflow and should also be recorded as ODA grants. For OOF reporting purposes, grants for commercial purposes such as subsidies to national private investors, and grants to forgive military debt, are also included. Grant-like flows are assimilated to grants. They comprise a) loans for which the service payments are to be made into an account in the borrowing country and used in the borrowing country for its own benefit, and b) provision of commodities for sale in the recipient\u2019s currency the proceeds of which are used in the recipient country for its own benefit.\n\nData are in current U.S. dollars.\n\nFor more information, please refer to the Converged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire at https://one.oecd.org/document/DCD/DAC/STAT(2023)9/FINAL/en/pdf.\n\nStatistical concept(s): Grants are wholly concessional by definition. All grants are reported as flows from the sector providing the funds for development or relief purposes. For their grant equivalents to be counted as official development assistance (ODA), the loans must be concessional i.e. bear a grant element of at least:\n\n\u2022 45 per cent in the case of bilateral loans to the official sector of least developed countries (LDCs) and other low-income countries (LICs).\n\u2022 15 per cent in the case of bilateral loans to the official sector of lower middle-income countries (LMICs).\n\u2022 10 per cent in the case of bilateral loans to the official sector of upper middle-income countries (UMICs), loans to multilateral institutions and loans to international non-governmental organizations (INGOs).\n\nLoans whose terms are not consistent with the IMF Debt Limits Policy and/or the World Bank\u2019s Non-Concessional Borrowing Policy/Sustainable Development Finance Policy are not reportable as ODA.\n\nLoans committed before 2018, qualifying under the rules valid at the time (25% threshold calculated at a 10% discount rate) but not qualifying under the new rules (new concessionality thresholds calculated using the new discount rates) is reportable as ODA. Loans committed before 2018, not qualifying under the rules valid at the time but qualifying under the new rules are reportable as OOF over their life time. Future reporting on repayments of those loans are recorded as OOF too, not as ODA.\n\n### Development relevance:\nThe OECD's aid statistics are completely transparent and publicly available to users. They seek to do the following: 1) Inform taxpayers in OECD members and other countries about what is being spent on aid overseas which enables the public to see what governments are doing with their money, 2) Allow users to understand the key characteristics of ODA to help inform the policies and programs of development co-operation providers in low- and middle-income countries, 3) Provide a comprehensive perspective on what ODA is doing worldwide, 4) Hold governments accountable for their international commitments and legal obligations on where and how to spend their aid to maximize results or benefit the neediest countries, 5) Showcase the contributions of providers outside of the members of the Development Assistance Committee (DAC) where many bilateral providers and multilateral agencies outside of the DAC have been reporting their statistics to the OECD on a voluntary basis, and 6) improve OECD's ability to provide a comprehensive perspective on development finance flows to partner countries.", "origins": [ { "producer": "Development Assistance Committee - OECD (DAC2A), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "DAC2A: Aid (ODA) disbursements to countries and regions, Organisation for Economic Co-operation and Development (OECD), uri: DSD_DAC2@DF_DAC2A, note: Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows, Development Co-operation Report, and OECD Data Explorer database. Data are available online at: https://data-explorer.oecd.org/., publisher: Organisation for Economic Co-operation and Development (OECD). Indicator BX.GRT.EXTA.CD.WD (https://data.worldbank.org/indicator/BX.GRT.EXTA.CD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GRT.EXTA.CD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_grt_tech_cd_wd": { "title": "Technical cooperation grants (BoP, current US$)", "description_from_producer": "Technical cooperation grants include free-standing technical cooperation grants, which are intended to finance the transfer of technical and managerial skills or of technology for the purpose of building up general national capacity without reference to any specific investment projects; and investment-related technical cooperation grants, which are provided to strengthen the capacity to execute specific investment projects. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The term technical co-operation covers a large variety of aid activities. Some technical co-operation is extended in the form of projects.\n\nNon-project technical co-operation comprises activities such as the supply of volunteers or experts, other technical assistance, provision of scholarships and imputed student costs. Many of these activities are funded through specific TC budget lines, which may or may not be administered by the main aid agency. The exact use of funds is seldom known at the commitment stage. Consequently, data on the sectoral and geographical breakdown of such programmes are often collected on a disbursement basis only. As disbursement data can be very detailed (one \u201cactivity\u201d corresponding to one individual expert or student), aggregation by recipient and sector (purpose code) is recommended prior to reporting to the CRS++. Grants of technical co-operation to individual countries include the actual or imputed costs of tuition in the reporting country of nationals of developing countries concerned.\n\nFree-standing technical co-operation comprises activities financed by a donor country whose primary purpose is to augment the level of knowledge, skills, technical know-how or productive aptitudes of the population of developing countries, i.e. increasing their stock of human intellectual capital, or their capacity for more effective use of their existing factor endowment. This relates essentially to activities that either enhance or supply human resources. It includes financing of students and trainees who are nationals of developing countries; experts, teachers, and volunteers; equipment and materials for training; research; development-oriented social and cultural programmes, etc. Associated supplies are also classified as technical co-operation.\n\nGrants are transfers in cash or in kind for which no legal debt is incurred by the recipient. For ODA reporting purposes, they also include forgiveness of non-military debt, support to non-governmental organisations, certain interest subsidies, and certain costs incurred in the implementation of aid. Grants to multilateral agencies intended to soften the terms of the latter\u2019s lending are a direct resource outflow and should also be recorded as ODA grants. For OOF reporting purposes, grants for commercial purposes such as subsidies to national private investors, and grants to forgive military debt, are also included. Grant-like flows are assimilated to grants. They comprise a) loans for which the service payments are to be made into an account in the borrowing country and used in the borrowing country for its own benefit, and b) provision of commodities for sale in the recipient\u2019s currency the proceeds of which are used in the recipient country for its own benefit.\n\nData are in current U.S. dollars.\n\n### Development relevance:\nThe OECD\u2019s aid statistics are completely transparent and publicly available to users. They seek to do the following: 1) Inform taxpayers in OECD members and other countries about what is being spent on aid overseas which enables the public to see what governments are doing with their money, 2) Allow users to understand the key characteristics of ODA to help inform the policies and programs of development co-operation providers in low- and middle-income countries, 3) Provides a comprehensive perspective on what ODA is doing worldwide, 4) Hold governments accountable for their international commitments and legal obligations on where and how to spend their aid to maximize results or benefit the neediest countries, 5) Showcase the contributions of providers outside of the members of the Development Assistance Committee (DAC) where many bilateral providers and multilateral agencies outside of the DAC have been reporting their statistics to the OECD on a voluntary basis, and 6) improve OECD\u2019s ability to provide a comprehensive perspective on development finance flows to partner countries.\n\n### Other notes:\nFor more information, please refer to the Converged Statistical Reporting Directives for the Creditor Reporting System (CRS) and the Annual DAC Questionnaire at https://one.oecd.org/document/DCD/DAC/STAT(2023)9/FINAL/en/pdf.\nStatistical concept(s): Grants are wholly concessional by definition. All grants are reported as flows from the sector providing the funds for development or relief purposes. For their grant equivalents to be counted as official development assistance (ODA), the loans must be concessional i.e. bear a grant element of at least:\n\n\u2022 45 per cent in the case of bilateral loans to the official sector of least developed countries (LDCs) and other low-income countries (LICs).\n\u2022 15 per cent in the case of bilateral loans to the official sector of lower middle-income countries (LMICs).\n\u2022 10 per cent in the case of bilateral loans to the official sector of upper middle-income countries (UMICs), loans to multilateral institutions and loans to international non-governmental organizations (INGOs).\n\nLoans whose terms are not consistent with the IMF Debt Limits Policy and/or the World Bank\u2019s Non-Concessional Borrowing Policy/Sustainable Development Finance Policy are not reportable as ODA.\n\nLoans committed before 2018, qualifying under the rules valid at the time (25% threshold calculated at a 10% discount rate) but not qualifying under the new rules (new concessionality thresholds calculated using the new discount rates) is reportable as ODA. Loans committed before 2018, not qualifying under the rules valid at the time but qualifying under the new rules are reportable as OOF over their life time. Future reporting on repayments of those loans are recorded as OOF too, not as ODA.", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "DAC2A: Aid (ODA) disbursements to countries and regions, Organisation for Economic Co-operation and Development (OECD), uri: DSD_DAC2@DF_DAC2A, note: Development Assistance Committee of the Organisation for Economic Co-operation and Development, Geographical Distribution of Financial Flows, Development Co-operation Report, and OECD Data Explorer database. Data are available online at: https://data-explorer.oecd.org/. Indicator BX.GRT.TECH.CD.WD (https://data.worldbank.org/indicator/BX.GRT.TECH.CD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GRT.TECH.CD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_gsr_ccis_cd": { "title": "ICT service exports (BoP, current US$)", "description_from_producer": "Information and communication technology service exports include computer and communications services (telecommunications and postal and courier services) and information services (computer data and news-related service transactions). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The balance of payments (BoP) is a double-entry accounting system that shows all flows of goods and services into and out of an economy; all transfers that are the counterpart of real resources or financial claims provided to or by the rest of the world without a quid pro quo, such as donations and grants; and all changes in residents' claims on and liabilities to nonresidents that arise from economic transactions. All transactions are recorded twice - once as a credit and once as a debit. In principle the net balance should be zero, but in practice the accounts often do not balance, requiring inclusion of a balancing item, net errors and omissions.\nThe concepts and definitions underlying the data are based on the sixth edition of the International Monetary Fund's (IMF) Balance of Payments Manual.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.CCIS.CD (https://data.worldbank.org/indicator/BX.GSR.CCIS.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.CCIS.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_gsr_ccis_zs": { "title": "ICT service exports (% of service exports, BoP)", "description_from_producer": "Information and communication technology service exports include computer and communications services (telecommunications and postal and courier services) and information services (computer data and news-related service transactions).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The balance of payments (BoP) is a double-entry accounting system that shows all flows of goods and services into and out of an economy; all transfers that are the counterpart of real resources or financial claims provided to or by the rest of the world without a quid pro quo, such as donations and grants; and all changes in residents' claims on and liabilities to nonresidents that arise from economic transactions. All transactions are recorded twice - once as a credit and once as a debit. In principle the net balance should be zero, but in practice the accounts often do not balance, requiring inclusion of a balancing item, net errors and omissions.\n\nThe concepts and definitions underlying the data are based on the sixth edition of the International Monetary Fund's (IMF) Balance of Payments Manual.\n\n### Development relevance:\nThe balance of payments records an economy's transactions with the rest of the world. Balance of payments accounts are divided into two groups: the current account, which records transactions in goods, services, income, and current transfers, and the capital and financial account, which records capital transfers, acquisition or disposal of non-produced, nonfinancial assets, and transactions in financial assets and liabilities.\n\n### Limitations and exceptions:\nDiscrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.CCIS.ZS (https://data.worldbank.org/indicator/BX.GSR.CCIS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.CCIS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of service exports, BoP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of service exports, BoP" } }, "bx_gsr_cmcp_zs": { "title": "Communications, computer, etc. (% of service exports, BoP)", "description_from_producer": "Communications, computer, information, and other services cover international telecommunications; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; personal, cultural, and recreational services; manufacturing services on physical inputs owned by others; and maintenance and repair services and government services not included elsewhere. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.CMCP.ZS (https://data.worldbank.org/indicator/BX.GSR.CMCP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.CMCP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of service exports, BoP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of service exports, BoP" } }, "bx_gsr_fcty_cd": { "title": "Primary income receipts (BoP, current US$)", "description_from_producer": "Primary income receipts refer to employee compensation paid to resident workers working abroad and investment income (receipts on direct investment, portfolio investment, other investments, and receipts on reserve assets). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.FCTY.CD (https://data.worldbank.org/indicator/BX.GSR.FCTY.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.FCTY.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_gsr_gnfs_cd": { "title": "Exports of goods and services (BoP, current US$)", "description_from_producer": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Limitations and exceptions:\nDiscrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.GNFS.CD (https://data.worldbank.org/indicator/BX.GSR.GNFS.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.GNFS.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_gsr_insf_zs": { "title": "Insurance and financial services (% of service exports, BoP)", "description_from_producer": "Insurance and financial services cover various types of insurance provided to nonresidents by resident insurance enterprises and vice versa, and financial intermediary and auxiliary services (except those of insurance enterprises and pension funds) exchanged between residents and nonresidents. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.INSF.ZS (https://data.worldbank.org/indicator/BX.GSR.INSF.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.INSF.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of service exports, BoP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of service exports, BoP" } }, "bx_gsr_mrch_cd": { "title": "Goods exports (BoP, current US$)", "description_from_producer": "Exports of goods occur when there are changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. Merchanting is reclassified from services to goods.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.MRCH.CD (https://data.worldbank.org/indicator/BX.GSR.MRCH.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.MRCH.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_gsr_nfsv_cd": { "title": "Service exports (BoP, current US$)", "description_from_producer": "Exports of services are services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. Manufacturing services on physical inputs owned by others (goods for processing in BPM5) and maintenance and repair services n.i.e. are reclassified from goods to services.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.NFSV.CD (https://data.worldbank.org/indicator/BX.GSR.NFSV.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.NFSV.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_gsr_royl_cd": { "title": "Charges for the use of intellectual property, receipts (BoP, current US$)", "description_from_producer": "Charges for the use of proprietary rights (such as patents, trademarks, copyrights, industrial processes and designs including trade secrets, franchises), and charges for licenses to reproduce or distribute (or both) intellectual property embodied in produced originals or prototypes (such as copyrights on books and manuscripts, computer software, cinematographic works, and sound recordings) and related rights (such as for live performances and television, cable, or satellite broadcast). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.ROYL.CD (https://data.worldbank.org/indicator/BX.GSR.ROYL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.ROYL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_gsr_totl_cd": { "title": "Exports of goods, services and primary income (BoP, current US$)", "description_from_producer": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. Primary income represents the return that accrues to institutional units for their contribution to the production process or for the provision of financial assets and renting natural resources to other institutional units. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.TOTL.CD (https://data.worldbank.org/indicator/BX.GSR.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_gsr_tran_zs": { "title": "Transport services (% of service exports, BoP)", "description_from_producer": "Transport services covers the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.TRAN.ZS (https://data.worldbank.org/indicator/BX.GSR.TRAN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.TRAN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of service exports, BoP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of service exports, BoP" } }, "bx_gsr_trvl_zs": { "title": "Travel services (% of service exports, BoP)", "description_from_producer": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service exports which are services provided by residents to non-residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the goods and services accounts of the balance of payments, which records the value of a country's exports and imports of goods and services over a period of time. It helps in understanding the trade balance, which is a key indicator of a country's economic health. This information is valuable for policymakers, economists, investors, and businesses in making informed decisions related to trade policies, investment strategies, and economic planning.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.GSR.TRVL.ZS (https://data.worldbank.org/indicator/BX.GSR.TRVL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.GSR.TRVL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of service exports, BoP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of service exports, BoP" } }, "bx_klt_dinv_cd_wd": { "title": "Foreign direct investment, net inflows (BoP, current US$)", "description_from_producer": "Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital. Direct investment is a category of cross-border investment associated with a resident in one economy having control or a significant degree of influence on the management of an enterprise that is resident in another economy. Ownership of 10 percent or more of the ordinary shares of voting stock is the criterion for determining the existence of a direct investment relationship. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments.\n\n### Development relevance:\nPrivate financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits.\n\n### Limitations and exceptions:\nFDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available.\n\n### Other notes:\nNote: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6).", "origins": [ { "producer": "International Monetary Fund (Balance of Payments), UNCTAD, and national sources, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments database, International Monetary Fund (IMF), note: International Monetary Fund, Balance of Payments database, supplemented by data from the United Nations Conference on Trade and Development and official national sources.;\nUN Conference on Trade and Development (UNCTAD);\nOfficial national sources. Indicator BX.KLT.DINV.CD.WD (https://data.worldbank.org/indicator/BX.KLT.DINV.CD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.KLT.DINV.CD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_klt_dinv_wd_gd_zs": { "title": "Foreign direct investment, net inflows (% of GDP)", "description_from_producer": "Foreign direct investment is the net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of earnings, other long-term capital, and short-term capital as shown in the balance of payments. This series shows net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors, and is divided by GDP.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources.\n\nThe internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results in a direct investment relationship, based on control and influence. Distinguished from other kinds of international investment, FDI is made to establish a lasting interest in or effective management control over an enterprise in another country. A lasting interest in an investment enterprise typically involves establishing warehouses, manufacturing facilities, and other permanent or long-term organizations abroad. Direct investments may take the form of greenfield investment, where the investor starts a new venture in a foreign country by constructing new operational facilities; joint venture, where the investor enters into a partnership agreement with a company abroad to establish a new enterprise; or merger and acquisition, where the investor acquires an existing enterprise abroad. The IMF suggests that investments should account for at least 10 percent of voting stock to be counted as FDI. In practice many countries set a higher threshold. Many countries fail to report reinvested earnings, and the definition of long-term loans differs among countries. BoP refers to Balance of Payments.\n\n### Development relevance:\nPrivate financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits.\n\n### Limitations and exceptions:\nFDI data do not give a complete picture of international investment in an economy. Balance of payments data on FDI do not include capital raised locally, an important source of investment financing in some developing countries. In addition, FDI data omit nonequity cross-border transactions such as intra-unit flows of goods and services.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available.\n\n### Other notes:\nNote: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6).", "origins": [ { "producer": "IMF International Financial Statistics and Balance of Payments; World Bank International Debt Statistics and GDP estimates; and OECD GDP estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics and Balance of Payments databases, International Monetary Fund (IMF);\nInternational Debt Statistics, World Bank (WB);\nWorld Bank GDP estimates, World Bank (WB);\nOECD GDP estimates, Organisation for Economic Co-operation and Development (OECD). Indicator BX.KLT.DINV.WD.GD.ZS (https://data.worldbank.org/indicator/BX.KLT.DINV.WD.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.KLT.DINV.WD.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "bx_pef_totl_cd_wd": { "title": "Portfolio equity, net inflows (BoP, current US$)", "description_from_producer": "Portfolio equity includes net inflows from equity securities other than those recorded as direct investment and including shares, stocks, depository receipts (American or global), and direct purchases of shares in local stock markets by foreign investors. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF).\n\nPortfolio equity investment is defined as cross-border transactions and positions involving equity securities, other than those included in direct investment or reserve assets. Equity securities are equity instruments that are negotiable and designed to be traded, usually on organized exchanges or \"over the counter.\" The negotiability of securities facilitates trading, allowing securities to be held by different parties during their lives. Negotiability allows investors to diversify their portfolios and to withdraw their investment readily. Included in portfolio investment are investment fund shares or units (that is, those issued by investment funds) that are evidenced by securities and that are not reserve assets or direct investment. Although they are negotiable instruments, exchange-traded financial derivatives are not included in portfolio investment because they are in their own category.\n\n### Development relevance:\nPrivate financial flows - equity and debt - account for the bulk of development finance. Equity flows comprise foreign direct investment (FDI) and portfolio equity. Debt flows are financing raised through bond issuance, bank lending, and supplier credits.\n\n### Limitations and exceptions:\nPortfolio investors typically have less of a role in the decision making of the enterprise with potentially important implications for future flows and for the volatility of the price and volume of positions. Portfolio investment differs from other investment in that it provides a direct way to access financial markets, and thus it can provide liquidity and flexibility. It is associated with financial markets and with their specialized service providers, such as exchanges, dealers, and regulators. The nature of financial derivatives as instruments through which risk is traded in its own right in financial markets sets them apart from other types of investment. Whereas other instruments may also have risk transfer elements, these other instruments also provide financial or other resources.\n\nThe volume of global private financial flows reported by the World Bank generally differs from that reported by other sources because of differences in sources, classification of economies, and method used to adjust and disaggregate reported information. In addition, particularly for debt financing, differences may also reflect how some installments of the transactions and certain offshore issuances are treated.\n\nData on equity flows are shown for all countries for which data are available.\n\n### Other notes:\nNote: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6).", "origins": [ { "producer": "International Monetary Fund (Balance of Payments) and World Bank (International Debt Statistics)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments database, International Monetary Fund (IMF);\nInternational Debt Statistics, World Bank (WB). Indicator BX.PEF.TOTL.CD.WD (https://data.worldbank.org/indicator/BX.PEF.TOTL.CD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.PEF.TOTL.CD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_trf_curr_cd": { "title": "Secondary income receipts (BoP, current US$)", "description_from_producer": "Secondary income refers to transfers recorded in the balance of payments whenever an economy provides or receives goods, services, income, or financial items without a quid pro quo. All transfers not considered to be capital are current. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.TRF.CURR.CD (https://data.worldbank.org/indicator/BX.TRF.CURR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.TRF.CURR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_trf_pwkr_cd": { "title": "Personal transfers, receipts (BoP, current US$)", "description_from_producer": "Personal transfers are current transfers, in cash or in kind, received by resident households from non-resident households. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Balance of payments statistics are compiled in accordance with international standards: Balance of Payments and International Investment Position Manual, 6th or 5th editions. Specific information on how countries compile their balance of payments statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The international accounts for an economy summarize the economic relationships between residents of that economy and nonresidents. They comprise the following:\n\n\n\n(a) the international investment position (IIP)\u2014a statement that shows at a point in time the value of: financial assets of residents of an economy that are claims on nonresidents or are gold bullion held as reserve assets; and the liabilities of residents of an economy to nonresidents;\n\n\n\n(b) the balance of payments\u2014a statement that summarizes economic transactions between residents and nonresidents during a specific time period; and\n\n\n\n(c) the other changes in financial assets and liabilities accounts\u2014a statement that shows other flows, such as valuation changes, that reconciles the balance of payments and IIP for a specific period, by showing changes due to economic events other than transactions between residents and nonresidents.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary income account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, personal transfers, which are part of the secondary income account, consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. For many economies, personal transfers represent a sizable and stable source of funds that sometimes exceed official aid or financial inflows from foreign direct investment. Personal transfers may have a significant impact on poverty reduction and can finance economic growth in receiving economies.This information is thus valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies.\n\n### Other notes:\nNote: Data are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6) and are only available from 2005 onwards. For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator BX.TRF.PWKR.CD (https://data.worldbank.org/indicator/BX.TRF.PWKR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.TRF.PWKR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "BoP, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "BoP, current US$" } }, "bx_trf_pwkr_dt_gd_zs": { "title": "Personal remittances, received (% of GDP)", "description_from_producer": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Balance of Payments (BOP) from the International Monetary Fund (IMF) serves as the primary source of information for personal transfers, which are categorized under secondary income, and for the compensation of employees, classified as primary income of the current account. Depending on data availability, references may be made to quarterly or annual figures.\n\nInformation from government agencies such as central banks and national statistical offices further complements the BOP data. When countries have missing data for certain years, this is addressed using methods like Last Observation Carried Forward (LOCF) and Next Observation Carried Backward (NOCB). If disaggregated data is unavailable, estimates are created based on historical ratios and trends.\n\nThe data is presented as a percentage of \"GDP (current US$)\" (NY.GDP.MKTP.CD), which is sourced from the World Bank's national accounts data and the OECD National Accounts data files.\n\n### Other notes:\nNote: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6).", "origins": [ { "producer": "World Bank staff estimates based on IMF and OECD data", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nIMF balance of payments data, International Monetary Fund (IMF);\nWorld Bank GDP estimates, World Bank (WB);\nOECD GDP estimates, Organisation for Economic Co-operation and Development (OECD). Indicator BX.TRF.PWKR.DT.GD.ZS (https://data.worldbank.org/indicator/BX.TRF.PWKR.DT.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.TRF.PWKR.DT.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "cm_mkt_indx_zg": { "title": "S&P Global Equity Indices (annual % change)", "description_from_producer": "S&P Global Equity Indices measure the U.S. dollar price change in the stock markets covered by the S&P BMI country indices.\n\n### Statistical concept and methodology:\nMethodology: The S&P Global Broad Market Index (BMI) series is a global index suite with a transparent, modular structure that has been fully float-adjusted since 1989. It includes more than 14,000 stocks from 25 developed and 24 emerging markets. It is a rules-based index that measures global stock market performance. The index covers all publicly listed equities with float-adjusted market values of US$ 100 million or more and that meet minimum liquidity criteria measured by median daily value traded figures. The S&P Global BMI is made up of the S&P Developed BMI and S&P Emerging BMI. Additional information about methodology can be found on the S&P Global website: https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-global-bmi-sp-ifci-indices.pdf\n\nThe percentage changes for France, Germany, Japan, the United Kingdom, and the United States refer to local stock market prices: CAC40, DAX, Nikkei, FTSE, S&P500.\nStatistical concept(s): Ratios of end-of-period levels in U.S. dollars over previous end-of-period values in U.S. dollars times 100.\n\n### Development relevance:\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information. This lowers transaction costs, which in turn improves resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks. Open economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets.\n\n### Limitations and exceptions:\nMarkets included in Standard & Poor's emerging markets category vary widely in level of development. Thus, it is best to look at the entire category to identify the most significant market trends. It is also useful to remember that stock market trends may be distorted by currency conversions, especially when a currency has registered a significant devaluation.\nIndex methodology details are available on the S&P Global website: https://www.spglobal.com/spdji/en/indices/equity/sp-global-bmi/#overview", "origins": [ { "producer": "S&P Global BMI - S&P Dow Jones Indices, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "S&P Global BMI, S&P Dow Jones Indices, uri: https://www.spglobal.com/spdji/en/index-family/equity/global-equity/sp-global-bmi/#overview. Indicator CM.MKT.INDX.ZG (https://data.worldbank.org/indicator/CM.MKT.INDX.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/CM.MKT.INDX.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % change", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % change" } }, "cm_mkt_lcap_cd": { "title": "Market capitalization of listed domestic companies (current US$)", "description_from_producer": "Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange).\nStatistical concept(s): Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange).\n\n### Development relevance:\nStock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations.\n\n### Limitations and exceptions:\nData cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards.\n\n### Other notes:\nStock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.", "origins": [ { "producer": "World Federation of Exchanges (WFE), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Federation of Exchanges database, World Federation of Exchanges (WFE). Indicator CM.MKT.LCAP.CD (https://data.worldbank.org/indicator/CM.MKT.LCAP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/CM.MKT.LCAP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "cm_mkt_lcap_gd_zs": { "title": "Market capitalization of listed domestic companies (% of GDP)", "description_from_producer": "Market capitalization (also known as market value) is the share price times the number of shares outstanding (including their several classes) for listed domestic companies. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies are excluded. Data are end of year values.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange).\nStatistical concept(s): Market capitalization figures include: shares of listed domestic companies; shares of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange); common and preferred shares of domestic companies; and shares without voting rights. Market capitalization figures exclude: collective investment funds ; rights, warrants, ETFs, convertible instruments ; options, futures ; foreign listed shares other than exclusively listed ones; companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status; and companies admitted to trading (i.e., companies whose shares are traded at the exchange but not listed at the exchange).\n\n### Development relevance:\nStock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations.\n\n### Limitations and exceptions:\nData cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards.\n\n### Other notes:\nStock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.", "origins": [ { "producer": "World Federation of Exchanges (WFE), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Federation of Exchanges database, World Federation of Exchanges (WFE). Indicator CM.MKT.LCAP.GD.ZS (https://data.worldbank.org/indicator/CM.MKT.LCAP.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/CM.MKT.LCAP.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "cm_mkt_ldom_no": { "title": "Listed domestic companies, total", "description_from_producer": "Listed domestic companies, including foreign companies which are exclusively listed, are those which have shares listed on an exchange at the end of the year. Investment funds, unit trusts, and companies whose only business goal is to hold shares of other listed companies, such as holding companies and investment companies, regardless of their legal status, are excluded. A company with several classes of shares is counted once. Only companies admitted to listing on the exchange are included.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: A company is considered domestic when it is incorporated in the same country as where the exchange is located. The only exception is the case of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange as defined in the domestic market capitalization definition).\nStatistical concept(s): A company is considered domestic when it is incorporated in the same country as where the exchange is located. The only exception is the case of foreign companies which are exclusively listed on an exchange (i.e., the foreign company is not listed on any other exchange as defined in the domestic market capitalization definition).\n\n### Development relevance:\nStock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations.\n\n### Limitations and exceptions:\nData cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards.\n\n### Other notes:\nStock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.", "origins": [ { "producer": "World Federation of Exchanges (WFE), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Federation of Exchanges database, World Federation of Exchanges (WFE). Indicator CM.MKT.LDOM.NO (https://data.worldbank.org/indicator/CM.MKT.LDOM.NO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/CM.MKT.LDOM.NO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "cm_mkt_trad_cd": { "title": "Stocks traded, total value (current US$)", "description_from_producer": "The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. For data before 2001, the WFE used two different approaches for the collection of trading data, depending on the individual stock exchange's market organization and rules. The first approach is the Trading System View (TSV). Stock exchanges adopting this view count only those transactions which pass through their trading system or trading floor. The TSV is generally adopted by exchanges which operate a centralized order book (order-driven market). Trades done by their members off the exchange are not included. The second approach is the Regulated Environment View (REV). Stock exchanges in this category include all transactions subject to supervision by the market authority, including transactions made by members, and sometimes non-members, on outside trading systems and transactions into foreign markets. Figures reported under the REV approach will be higher than those reported under the TSV approach.\nStatistical concept(s): The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates. The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. https://data.worldbank.org/indicator/CM.MKT.TRAD.CD\n\n### Development relevance:\nStock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations.\n\n### Limitations and exceptions:\nData cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards. Only EOB trades are included in the total value of shares traded.\n\n### Other notes:\nStock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.", "origins": [ { "producer": "World Federation of Exchanges (WFE), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Federation of Exchanges database, World Federation of Exchanges (WFE). Indicator CM.MKT.TRAD.CD (https://data.worldbank.org/indicator/CM.MKT.TRAD.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/CM.MKT.TRAD.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "cm_mkt_trad_gd_zs": { "title": "Stocks traded, total value (% of GDP)", "description_from_producer": "The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. For data before 2001, the WFE used two different approaches for the collection of trading data, depending on the individual stock exchange's market organization and rules. The first approach is the Trading System View (TSV). Stock exchanges adopting this view count only those transactions which pass through their trading system or trading floor. The TSV is generally adopted by exchanges which operate a centralized order book (order-driven market). Trades done by their members off the exchange are not included. The second approach is the Regulated Environment View (REV). Stock exchanges in this category include all transactions subject to supervision by the market authority, including transactions made by members, and sometimes non-members, on outside trading systems and transactions into foreign markets. Figures reported under the REV approach will be higher than those reported under the TSV approach.\nStatistical concept(s): The value of shares traded represent the transfer of ownership effected automatically through the exchange's electronic order book (EOB), where orders placed by trading members are usually exposed to all market users and automatically matched according to precise rules set up by the exchange, generally on a price/time priority basis. For data before 2001, the WFE used two different approaches for the collection of trading data, depending on the individual stock exchange's market organization and rules. The first approach is the Trading System View (TSV). Stock exchanges adopting this view count only those transactions which pass through their trading system or trading floor. The TSV is generally adopted by exchanges which operate a centralized order book (order-driven market). Trades done by their members off the exchange are not included. The second approach is the Regulated Environment View (REV). Stock exchanges in this category include all transactions subject to supervision by the market authority, including transactions made by members, and sometimes non-members, on outside trading systems and transactions into foreign markets. Figures reported under the REV approach will be higher than those reported under the TSV approach.\n\n### Development relevance:\nStock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations.\n\n### Limitations and exceptions:\nData cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards. Only EOB trades are included in the total value of shares traded.\n\n### Other notes:\nStock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.", "origins": [ { "producer": "World Federation of Exchanges (WFE), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Federation of Exchanges database, World Federation of Exchanges (WFE). Indicator CM.MKT.TRAD.GD.ZS (https://data.worldbank.org/indicator/CM.MKT.TRAD.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/CM.MKT.TRAD.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "cm_mkt_trnr": { "title": "Stocks traded, turnover ratio of domestic shares (%)", "description_from_producer": "Turnover ratio is the value of domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Turnover ratio is the value of electronic order book (EOB) domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12, according to the following formula: (Monthly EOB domestic shares traded / Month-end domestic market capitalization) x 12.\nStatistical concept(s): Statistical Concept and Methodology: Turnover ratio is the value of electronic order book (EOB) domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12, according to the following formula: (Monthly EOB domestic shares traded / Month-end domestic market capitalization) x 12.\n\n### Development relevance:\nStock market size can be measured in various ways, and each may produce a different ranking of countries.\n\nThe development of an economy's financial markets is closely related to its overall development. Well-functioning financial systems provide good and easily accessible information which can lower transaction costs and subsequently improve resource allocation and boosts economic growth. Both banking systems and stock markets enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient relative to domestic banks.\n\nOpen economies with sound macroeconomic policies, good legal systems, and shareholder protection attract capital and therefore have larger financial markets. Recent research on stock market development shows that modern communications technology and increased financial integration have resulted in more cross-border capital flows, a stronger presence of financial firms around the world, and the migration of stock exchange activities to international exchanges. Many firms in emerging markets now cross-list on international exchanges, which provides them with lower cost capital and more liquidity-traded shares. However, this also means that exchanges in emerging markets may not have enough financial activity to sustain them, putting pressure on them to rethink their operations.\n\n### Limitations and exceptions:\nData cover measures of size (market capitalization, number of listed domestic companies) and liquidity (value of shares traded as a percentage of gross domestic product, value of shares traded as a percentage of market capitalization). The comparability of such data across countries may be limited by conceptual and statistical weaknesses, such as inaccurate reporting and differences in accounting standards. Only domestic shares are used in order to be consistent with domestic market capitalization.\n\n### Other notes:\nStock market data were previously sourced from Standard & Poor's until they discontinued their \"Global Stock Markets Factbook\" and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.", "origins": [ { "producer": "World Federation of Exchanges (WFE), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Federation of Exchanges database, World Federation of Exchanges (WFE). Indicator CM.MKT.TRNR (https://data.worldbank.org/indicator/CM.MKT.TRNR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/CM.MKT.TRNR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "dc_dac_ausl_cd": { "title": "Net bilateral aid flows from DAC donors, Australia (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.AUSL.CD (https://data.worldbank.org/indicator/DC.DAC.AUSL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.AUSL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_autl_cd": { "title": "Net bilateral aid flows from DAC donors, Austria (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.AUTL.CD (https://data.worldbank.org/indicator/DC.DAC.AUTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.AUTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_bell_cd": { "title": "Net bilateral aid flows from DAC donors, Belgium (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.BELL.CD (https://data.worldbank.org/indicator/DC.DAC.BELL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.BELL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_canl_cd": { "title": "Net bilateral aid flows from DAC donors, Canada (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.CANL.CD (https://data.worldbank.org/indicator/DC.DAC.CANL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.CANL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_cecl_cd": { "title": "Net bilateral aid flows from DAC donors, European Union institutions (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.CECL.CD (https://data.worldbank.org/indicator/DC.DAC.CECL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.CECL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_chel_cd": { "title": "Net bilateral aid flows from DAC donors, Switzerland (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.CHEL.CD (https://data.worldbank.org/indicator/DC.DAC.CHEL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.CHEL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_czel_cd": { "title": "Net bilateral aid flows from DAC donors, Czechia (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.CZEL.CD (https://data.worldbank.org/indicator/DC.DAC.CZEL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.CZEL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_deul_cd": { "title": "Net bilateral aid flows from DAC donors, Germany (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.DEUL.CD (https://data.worldbank.org/indicator/DC.DAC.DEUL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.DEUL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_dnkl_cd": { "title": "Net bilateral aid flows from DAC donors, Denmark (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.DNKL.CD (https://data.worldbank.org/indicator/DC.DAC.DNKL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.DNKL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_espl_cd": { "title": "Net bilateral aid flows from DAC donors, Spain (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.ESPL.CD (https://data.worldbank.org/indicator/DC.DAC.ESPL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.ESPL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_estl_cd": { "title": "Net bilateral aid flows from DAC donors, Estonia (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 32 members - 31 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.ESTL.CD (https://data.worldbank.org/indicator/DC.DAC.ESTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.ESTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dc_dac_finl_cd": { "title": "Net bilateral aid flows from DAC donors, Finland (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.FINL.CD (https://data.worldbank.org/indicator/DC.DAC.FINL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.FINL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_fral_cd": { "title": "Net bilateral aid flows from DAC donors, France (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.FRAL.CD (https://data.worldbank.org/indicator/DC.DAC.FRAL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.FRAL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_gbrl_cd": { "title": "Net bilateral aid flows from DAC donors, United Kingdom (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.GBRL.CD (https://data.worldbank.org/indicator/DC.DAC.GBRL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.GBRL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_grcl_cd": { "title": "Net bilateral aid flows from DAC donors, Greece (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.GRCL.CD (https://data.worldbank.org/indicator/DC.DAC.GRCL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.GRCL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_hunl_cd": { "title": "Net bilateral aid flows from DAC donors, Hungary (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.HUNL.CD (https://data.worldbank.org/indicator/DC.DAC.HUNL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.HUNL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dc_dac_irll_cd": { "title": "Net bilateral aid flows from DAC donors, Ireland (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.IRLL.CD (https://data.worldbank.org/indicator/DC.DAC.IRLL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.IRLL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_isll_cd": { "title": "Net bilateral aid flows from DAC donors, Iceland (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.ISLL.CD (https://data.worldbank.org/indicator/DC.DAC.ISLL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.ISLL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_ital_cd": { "title": "Net bilateral aid flows from DAC donors, Italy (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.ITAL.CD (https://data.worldbank.org/indicator/DC.DAC.ITAL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.ITAL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_jpnl_cd": { "title": "Net bilateral aid flows from DAC donors, Japan (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.JPNL.CD (https://data.worldbank.org/indicator/DC.DAC.JPNL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.JPNL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_korl_cd": { "title": "Net bilateral aid flows from DAC donors, Korea, Rep. (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.KORL.CD (https://data.worldbank.org/indicator/DC.DAC.KORL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.KORL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_ltul_cd": { "title": "Net bilateral aid flows from DAC donors, Lithuania (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Lithuania, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 32 members - 31 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.LTUL.CD (https://data.worldbank.org/indicator/DC.DAC.LTUL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.LTUL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dc_dac_luxl_cd": { "title": "Net bilateral aid flows from DAC donors, Luxembourg (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.LUXL.CD (https://data.worldbank.org/indicator/DC.DAC.LUXL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.LUXL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_nldl_cd": { "title": "Net bilateral aid flows from DAC donors, Netherlands (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.NLDL.CD (https://data.worldbank.org/indicator/DC.DAC.NLDL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.NLDL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_norl_cd": { "title": "Net bilateral aid flows from DAC donors, Norway (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.NORL.CD (https://data.worldbank.org/indicator/DC.DAC.NORL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.NORL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_nzll_cd": { "title": "Net bilateral aid flows from DAC donors, New Zealand (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.NZLL.CD (https://data.worldbank.org/indicator/DC.DAC.NZLL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.NZLL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_poll_cd": { "title": "Net bilateral aid flows from DAC donors, Poland (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.POLL.CD (https://data.worldbank.org/indicator/DC.DAC.POLL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.POLL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_prtl_cd": { "title": "Net bilateral aid flows from DAC donors, Portugal (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.PRTL.CD (https://data.worldbank.org/indicator/DC.DAC.PRTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.PRTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_svkl_cd": { "title": "Net bilateral aid flows from DAC donors, Slovak Republic (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.SVKL.CD (https://data.worldbank.org/indicator/DC.DAC.SVKL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.SVKL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_svnl_cd": { "title": "Net bilateral aid flows from DAC donors, Slovenia (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.SVNL.CD (https://data.worldbank.org/indicator/DC.DAC.SVNL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.SVNL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_swel_cd": { "title": "Net bilateral aid flows from DAC donors, Sweden (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.SWEL.CD (https://data.worldbank.org/indicator/DC.DAC.SWEL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.SWEL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_totl_cd": { "title": "Net bilateral aid flows from DAC donors, Total (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.TOTL.CD (https://data.worldbank.org/indicator/DC.DAC.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_dac_usal_cd": { "title": "Net bilateral aid flows from DAC donors, United States (current US$)", "description_from_producer": "Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the Development Assistance Committee (DAC). Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. DAC members are Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, The Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom, United States, and European Union Institutions. Regional aggregates include data for economies not specified elsewhere. World and income group totals include aid not allocated by country or region. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) has 30 members - 29 individual economies and 1 multilateral institution (European Union institutions).\n\nData are based on donor country reports of bilateral programs, which may differ from reports by recipient countries. Recipients may lack access to information on such aid expenditures as development-oriented research, stipends and tuition costs for aid-financed students in donor countries, and payment of experts hired by donor countries. Moreover, a full accounting would include donor country contributions to multilateral institutions, the flow of resources from multilateral institutions to recipient countries, and flows from countries that are not members of DAC.\n\nSome of the aid recipients are also aid donors. Development cooperation activities by non-DAC members have increased in recent years and in some cases surpass those of individual DAC members. Some non-DAC donors report their development cooperation activities to DAC on a voluntary basis, but many do not yet report their aid flows to DAC.\n\n### Limitations and exceptions:\nData exclude DAC members\u2019 multilateral aid (contributions to the regular budgets of the multilateral institutions). However, projects executed by multilateral institutions or nongovernmental organizations on behalf of DAC members are classified as bilateral aid (since the donor country effectively controls the use of the funds) and are included in the data.\n\nAid to unspecified economies is included in regional totals and, when possible, income group totals. Aid not allocated by country or region - including administrative costs, research on development, and aid to nongovernmental organizations - is included in the world total. Thus regional and income group totals do not sum to the world total.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DC.DAC.USAL.CD (https://data.worldbank.org/indicator/DC.DAC.USAL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.DAC.USAL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_oda_tldc_cd": { "title": "Net ODA provided, to the least developed countries (current US$)", "description_from_producer": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council.\n\n### Statistical concept and methodology:\nMethodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline. Indicator DC.ODA.TLDC.CD (https://data.worldbank.org/indicator/DC.ODA.TLDC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.ODA.TLDC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_oda_tldc_gn_zs": { "title": "Net ODA provided to the least developed countries (% of GNI)", "description_from_producer": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. The list of least developed countries (LDCs) has been agreed by the General Assembly, on the recommendation of the Committee for Development Policy, Economic and Social Council. Series is shown as a share of donors' GNI.\n\n### Statistical concept and methodology:\nMethodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is presented as a percentage of Gross National Income (GNI).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline. Indicator DC.ODA.TLDC.GN.ZS (https://data.worldbank.org/indicator/DC.ODA.TLDC.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.ODA.TLDC.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "dc_oda_totl_cd": { "title": "Net ODA provided, total (current US$)", "description_from_producer": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms.\n\n### Statistical concept and methodology:\nMethodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline. Indicator DC.ODA.TOTL.CD (https://data.worldbank.org/indicator/DC.ODA.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.ODA.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dc_oda_totl_gn_zs": { "title": "Net ODA provided, total (% of GNI)", "description_from_producer": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. It is shown as a share of donors' GNI.\n\n### Statistical concept and methodology:\nMethodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is presented as a percentage of Gross National Income (GNI).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline. Indicator DC.ODA.TOTL.GN.ZS (https://data.worldbank.org/indicator/DC.ODA.TOTL.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.ODA.TOTL.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "dc_oda_totl_kd": { "title": "Net ODA provided, total (constant 2023 US$)", "description_from_producer": "Net Official development assistance (ODA) comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. Data are in constant 2023 U.S. dollars.\n\n### Statistical concept and methodology:\nMethodology: Disbursements by donors include both bilateral Official Development Assistance (ODA) flows to recipient countries and multilateral ODA contributions to eligible organizations. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in constant U.S. dollar prices to account for inflation in the donor's currency and the changes in exchange rates with the U.S. dollar.", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline. Indicator DC.ODA.TOTL.KD (https://data.worldbank.org/indicator/DC.ODA.TOTL.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DC.ODA.TOTL.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2023 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2023 US$" } }, "dt_dod_dect_cd": { "title": "External debt stocks, total (DOD, current US$)", "description_from_producer": "Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\nStatistical concept(s): Disbursed and outstanding debt definition (??)\nDebt stock definition (??)\n\n### Development relevance:\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.DECT.CD (https://data.worldbank.org/indicator/DT.DOD.DECT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.DECT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "DOD, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "DOD, current US$" } }, "dt_dod_dect_gn_zs": { "title": "External debt stocks (% of GNI)", "description_from_producer": "Total external debt stocks to gross national income. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio\nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.DECT.GN.ZS (https://data.worldbank.org/indicator/DT.DOD.DECT.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.DECT.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "dt_dod_dimf_cd": { "title": "Use of IMF credit (DOD, current US$)", "description_from_producer": "Use of IMF Credit: Data related to the operations of the IMF are provided by the IMF Treasurer\u2019s Department. They are converted from special drawing rights into dollars using end-of-period exchange rates for stocks and average-over-the-period exchange rates for flows. IMF trust fund operations under the Enhanced Structural Adjustment Facility, Extended Fund Facility, Poverty Reduction and Growth Facility, and Structural Adjustment Facility (Enhanced Structural Adjustment Facility in 1999) are presented together with all of the IMF\u2019s special facilities (buffer stock, supplemental reserve, compensatory and contingency facilities, oil facilities, and other facilities). SDR allocations are also included in this category. According to the BPM6, SDR allocations are recorded as the incurrence of a debt liability of the member receiving them (because of a requirement to repay the allocation in certain circumstances, and also because interest accrues). This debt item is introduced for the first time this year with historical data starting in 1999.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data related to the operations of the IMF come from the IMF Treasurer's Department and are converted from special drawing rights (SDRs) into dollars using end-of-period exchange rates for stocks and average over the period exchange rates for converting flows. DOD refers to disbursed and outstanding debt; data are in current U.S. dollars.\n\nData on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.DIMF.CD (https://data.worldbank.org/indicator/DT.DOD.DIMF.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.DIMF.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "DOD, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "DOD, current US$" } }, "dt_dod_dlxf_cd": { "title": "External debt stocks, long-term (DOD, current US$)", "description_from_producer": "Long-term debt is debt that has an original or extended maturity of more than one year. It has three components: public, publicly guaranteed, and private nonguaranteed debt. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.DLXF.CD (https://data.worldbank.org/indicator/DT.DOD.DLXF.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.DLXF.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "DOD, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "DOD, current US$" } }, "dt_dod_dpng_cd": { "title": "External debt stocks, private nonguaranteed (PNG) (DOD, current US$)", "description_from_producer": "Private nonguaranteed external debt comprises long-term external obligations of private debtors that are not guaranteed for repayment by a public entity. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.DPNG.CD (https://data.worldbank.org/indicator/DT.DOD.DPNG.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.DPNG.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "DOD, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "DOD, current US$" } }, "dt_dod_dppg_cd": { "title": "External debt stocks, public and publicly guaranteed (PPG) (DOD, current US$)", "description_from_producer": "Public and publicly guaranteed debt comprises long-term external obligations of public debtors, including the national government, Public Corporations, State Owned Enterprises, Development Banks and Other Mixed Enterprises, political subdivisions (or an agency of either), autonomous public bodies, and external obligations of private debtors that are guaranteed for repayment by a public entity. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.DPPG.CD (https://data.worldbank.org/indicator/DT.DOD.DPPG.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.DPPG.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "DOD, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "DOD, current US$" } }, "dt_dod_dstc_cd": { "title": "External debt stocks, short-term (DOD, current US$)", "description_from_producer": "Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.DSTC.CD (https://data.worldbank.org/indicator/DT.DOD.DSTC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.DSTC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "DOD, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "DOD, current US$" } }, "dt_dod_dstc_ir_zs": { "title": "Short-term debt (% of total reserves)", "description_from_producer": "Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Total reserves includes gold.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio\nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock\n\n### Limitations and exceptions:\nThe DRS encourages debtor countries to voluntarily provide information on their short-term external obligations. By its nature, short-term external debt is difficult to monitor: loan-by-loan registration is normally impractical, and monitoring systems typically rely on information requested periodically by the central bank from the banking sector. The World Bank regards the debtor country as the authoritative source of information on its short-term debt. Where such information is not available from the debtor country, data are derived from BIS data on international bank lending based on time remaining to original maturity. The data are reported based on residual maturity, but an estimate of short-term external liabilities by original maturity can be derived by deducting from claims due in one year those that have a maturity of between one and two years. However, BIS data include liabilities reported only by banks within the BIS reporting area. The results should thus be interpreted with caution. Because short-term debt poses an immediate burden and is particularly important for monitoring vulnerability, it is compared with total debt and foreign exchange reserves, which are instrumental in providing coverage for such obligations.\n\nA country's external debt burden, both debt outstanding and debt service, affects its creditworthiness and vulnerability. While data related to public and publicly guaranteed debt are reported to the DRS on a loan-by-loan basis, aggregate data on long-term private nonguaranteed debt are reported annually and are reported by the country or estimated by World Bank staff for countries where this type of external debt is known to be significant. Estimates are based on national data from the World Bank's Quarterly External Debt Statistics.", "origins": [ { "producer": "World Bank, IMF (Balance of Payments Statistics), and OECD", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Development Indicators, World Bank (WB);\nInternational Monetary Fund (IMF), type: Balance of Payments Statistics Yearbook and data files;\nWorld Bank (WB), type: GDP estimates;\nOrganisation for Economic Co-operation and Development (OECD), type: GDP estimates. Indicator DT.DOD.DSTC.IR.ZS (https://data.worldbank.org/indicator/DT.DOD.DSTC.IR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.DSTC.IR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total reserves", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total reserves" } }, "dt_dod_dstc_xp_zs": { "title": "Short-term debt (% of exports of goods, services and primary income)", "description_from_producer": "Short-term external debt is defined as debt that has an original maturity of one year or less. Available data permit no distinction between public and private nonguaranteed short-term debt. Exports of goods, services and primary income is the sum of goods (merchandise) exports, exports of (nonfactor) services and income (factor) receipts.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\nThe data is presented as a percentage of \"Exports of goods, services and primary income (BoP, current US$)\" (BX.GSR.TOTL.CD), sourced from the International Monetary Fund's Balance of Payments Statistics Yearbook and data files.\n\n### Development relevance:\nExternal debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio\nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.DSTC.XP.ZS (https://data.worldbank.org/indicator/DT.DOD.DSTC.XP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.DSTC.XP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of exports of goods, services and primary income", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of exports of goods, services and primary income" } }, "dt_dod_dstc_zs": { "title": "Short-term debt (% of total external debt)", "description_from_producer": "Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio\nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.DSTC.ZS (https://data.worldbank.org/indicator/DT.DOD.DSTC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.DSTC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total external debt", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total external debt" } }, "dt_dod_mibr_cd": { "title": "PPG, IBRD (DOD, current US$)", "description_from_producer": "Public and publicly guaranteed debt outstanding from the International Bank for Reconstruction and Development (IBRD) is nonconcessional. Nonconcessional debt excludes loans with an original grant element of 35 percent or more. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.MIBR.CD (https://data.worldbank.org/indicator/DT.DOD.MIBR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.MIBR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "DOD, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "DOD, current US$" } }, "dt_dod_mida_cd": { "title": "PPG, IDA (DOD, current US$)", "description_from_producer": "Public and publicly guaranteed debt outstanding from the International Development Association (IDA) is concessional. Concessional debt is defined as loans with an original grant element of 35 percent or more. The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 5 percent. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.MIDA.CD (https://data.worldbank.org/indicator/DT.DOD.MIDA.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.MIDA.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "DOD, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "DOD, current US$" } }, "dt_dod_mwbg_cd": { "title": "IBRD loans and IDA credits (DOD, current US$)", "description_from_producer": "IBRD loans and IDA credits are public and publicly guaranteed debt extended by the World Bank Group. The International Bank for Reconstruction and Development (IBRD) lends at market rates. Credits from the International Development Association (IDA) are at concessional rates. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.DOD.MWBG.CD (https://data.worldbank.org/indicator/DT.DOD.MWBG.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.MWBG.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "DOD, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "DOD, current US$" } }, "dt_dod_pvlx_cd": { "title": "Present value of external debt (current US$)", "description_from_producer": "Present value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries' reports on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nPresent value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. The PV of external debt is a better measure for debt burdens of countries that have access to concessional financing. The IMF/World Bank's Low-Income Countries (LICs) Debt Sustainability Framework (DSF) uses the present value of external debt as a means to assess a country's risk of external and overall debt distress.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio\nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics. Indicator DT.DOD.PVLX.CD (https://data.worldbank.org/indicator/DT.DOD.PVLX.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.PVLX.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_dod_pvlx_ex_zs": { "title": "Present value of external debt (% of exports of goods, services and primary income)", "description_from_producer": "Present value of external debt to exports of goods, services and income. Present value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan. Exports of goods, services and primary income is the sum of goods (merchandise) exports, exports of (nonfactor) services and income (factor) receipts. The exports denominator is a three-year average.\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nPresent value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. The PV of external debt is a better measure for debt burden's of countries that have access to concessional financing. The IMF/World Bank's Low-Income Countries (LICs) Debt Sustainability Framework (DSF) uses the present value of external debt as a means to assess a country's risk of external and overall debt distress.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio\nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics. Indicator DT.DOD.PVLX.EX.ZS (https://data.worldbank.org/indicator/DT.DOD.PVLX.EX.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.PVLX.EX.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of exports of goods, services and income", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of exports of goods, services and income" } }, "dt_dod_pvlx_gn_zs": { "title": "Present value of external debt (% of GNI)", "description_from_producer": "Present value of external debt to gross national income. Present value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. This calculation assumes that the PV of loans with a negative grant element is equal to the nominal value of the loan. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad. The GNI denominator is a three-year average.\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nPresent value of debt is the sum of short-term external debt plus the discounted sum of total debt service payments due on public, publicly guaranteed, and private nonguaranteed long-term external debt over the life of existing loans. The PV of external debt is a better measure for debt burden's of countries that have access to concessional financing. The IMF/World Bank's Low-Income Countries (LICs) Debt Sustainability Framework (DSF) uses the present value of external debt as a means to assess a country's risk of external and overall debt distress.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio\nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics. Indicator DT.DOD.PVLX.GN.ZS (https://data.worldbank.org/indicator/DT.DOD.PVLX.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.DOD.PVLX.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_blat_cd": { "title": "Net financial flows, bilateral (NFL, current US$)", "description_from_producer": "Bilateral debt includes loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data show concessional and nonconcessional financial flows from official bilateral sources. The Organisation for Economic Co-operation and Development's (OECD) Development Assistance Committee (DAC) defines concessional flows from bilateral donors as flows with a grant element of at least 25 percent; they are evaluated assuming a 10 percent nominal discount rate.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.BLAT.CD (https://data.worldbank.org/indicator/DT.NFL.BLAT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.BLAT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_bond_cd": { "title": "Portfolio investment, bonds (PPG + PNG) (NFL, current US$)", "description_from_producer": "Bonds are securities issued with a fixed rate of interest for a period of more than one year. They include net flows through cross-border public and publicly guaranteed and private nonguaranteed bond issues. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Bonds are debt instruments issued by public and publicly guaranteed or private debtors with durations of one year or longer. Bonds usually give the holder the unconditional right to fixed money income or contractually determined, variable money income.\n\nData on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels.\n\n### Limitations and exceptions:\nThe DRS encourages debtor countries to voluntarily provide information on their short-term external obligations. By its nature, short-term external debt is difficult to monitor: loan-by-loan registration is normally impractical, and monitoring systems typically rely on information requested periodically by the central bank from the banking sector. The World Bank regards the debtor country as the authoritative source of information on its short-term debt. Where such information is not available from the debtor country, data are derived from BIS data on international bank lending based on time remaining to original maturity. The data are reported based on residual maturity, but an estimate of short-term external liabilities by original maturity can be derived by deducting from claims due in one year those that have a maturity of between one and two years. However, BIS data include liabilities reported only by banks within the BIS reporting area. The results should thus be interpreted with caution. Because short-term debt poses an immediate burden and is particularly important for monitoring vulnerability, it is compared with total debt and foreign exchange reserves, which are instrumental in providing coverage for such obligations.\n\nA country's external debt burden, both debt outstanding and debt service, affects its creditworthiness and vulnerability. While data related to public and publicly guaranteed debt are reported to the DRS on a loan-by-loan basis, aggregate data on long-term private nonguaranteed debt are reported annually and are reported by the country or estimated by World Bank staff for countries where this type of external debt is known to be significant. Estimates are based on national data from the World Bank's Quarterly External Debt Statistics.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.BOND.CD (https://data.worldbank.org/indicator/DT.NFL.BOND.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.BOND.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_cerf_cd": { "title": "Net official flows from UN agencies, CERF (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United Nations Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Development relevance:\nOfficial development assistance (ODA) is defined as government aid that promotes and specifically targets the economic development and welfare of developing countries. The DAC adopted ODA as the \u201cgold standard\u201d of foreign aid in 1969 and it remains the main source of financing for development aid. ODA data is collected, verified and made publicly available by the OECD. The DAC has measured resource flows to developing countries since 1961. Special attention has been given to the official and concessional part of this flow, defined as \u201cofficial development assistance\u201d (ODA). The DAC first defined ODA in 1969, and tightened the definition in 1972. ODA is the key measure used in practically all aid targets and assessments of aid performance.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.CERF.CD (https://data.worldbank.org/indicator/DT.NFL.CERF.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.CERF.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_dpng_cd": { "title": "Net flows on external debt, private nonguaranteed (PNG) (NFL, current US$)", "description_from_producer": "Private nonguaranteed external debt is an external obligation of a private debtor that is not guaranteed for repayment by a public entity. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Countries that report to the World Bank\u2019s Debtor Reporting System (DRS) submit their private\u00a0nonguaranteed\u00a0(PNG) on an aggregate basis. The Debt Data Team then compiles this information to produce this indicator.\n\n### Development relevance:\nThis indicator tells us the net flows (disbursements minus principal payments) of private debtor's external debt that is not guaranteed for repayment by a public entity.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics. Indicator DT.NFL.DPNG.CD (https://data.worldbank.org/indicator/DT.NFL.DPNG.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.DPNG.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_faog_cd": { "title": "Net official flows from UN agencies, FAO (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.FAOG.CD (https://data.worldbank.org/indicator/DT.NFL.FAOG.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.FAOG.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_iaea_cd": { "title": "Net official flows from UN agencies, IAEA (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.IAEA.CD (https://data.worldbank.org/indicator/DT.NFL.IAEA.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.IAEA.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_ifad_cd": { "title": "Net official flows from UN agencies, IFAD (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.IFAD.CD (https://data.worldbank.org/indicator/DT.NFL.IFAD.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.IFAD.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_ilog_cd": { "title": "Net official flows from UN agencies, ILO (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies include the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.ILOG.CD (https://data.worldbank.org/indicator/DT.NFL.ILOG.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.ILOG.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_imfc_cd": { "title": "Net financial flows, IMF concessional (NFL, current US$)", "description_from_producer": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides concessional lending through the Poverty Reduction and Growth Facility and the IMF Trust Fund. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The International Monetary Fund (IMF) makes concessional funds available through its Extended Credit Facility (which replaced the Poverty Reduction and Growth Facility in 2010), the Standby Credit Facility, and the Rapid Credit Facility. Eligibility is based principally on a country's per capita income and eligibility under IDA.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.IMFC.CD (https://data.worldbank.org/indicator/DT.NFL.IMFC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.IMFC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_imfn_cd": { "title": "Net financial flows, IMF nonconcessional (NFL, current US$)", "description_from_producer": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IMF is the International Monetary Fund, which provides nonconcessional lending through the credit it provides to its members, mainly to meet balance of payments needs. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Nonconcessional lending from the IMF is provided mainly through Stand-by Arrangements, the Flexible Credit Line, and the Extended Fund Facility.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.IMFN.CD (https://data.worldbank.org/indicator/DT.NFL.IMFN.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.IMFN.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_mibr_cd": { "title": "Net financial flows, IBRD (NFL, current US$)", "description_from_producer": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IBRD is the International Bank for Reconstruction and Development, the founding and largest member of the World Bank Group. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The World Bank's International Bank for Reconstruction and Development (IBRD) lends to creditworthy countries at a variable base rate of six-month LIBOR plus a spread, either variable or fixed, for the life of the loan. The rate is reset every six months and applies to the interest period beginning on that date.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.MIBR.CD (https://data.worldbank.org/indicator/DT.NFL.MIBR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.MIBR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_mida_cd": { "title": "Net financial flows, IDA (NFL, current US$)", "description_from_producer": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. IDA is the International Development Association, the concessional loan window of the World Bank Group. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: World Bank concessional lending is done by the International Development Association (IDA) based on gross national income (GNI) per capita and performance standards assessed by World Bank staff.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.MIDA.CD (https://data.worldbank.org/indicator/DT.NFL.MIDA.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.MIDA.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_mlat_cd": { "title": "Net financial flows, multilateral (NFL, current US$)", "description_from_producer": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data show concessional and nonconcessional financial flows from international financial institutions. International financial institutions fund nonconcessional lending operations primarily by selling low-interest, highly rated bonds backed by prudent lending and financial policies and the strong financial support of their members. Funds are then on-lent to developing countries at slightly higher interest rates with 15- to 20-year maturities. Lending terms vary with market conditions and institutional policies. Concessional flows from international financial institutions are credits provided through concessional lending facilities. Subsidies from donors or other resources reduce the cost of these loans. Grants are not included in net flows.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.MLAT.CD (https://data.worldbank.org/indicator/DT.NFL.MLAT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.MLAT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_moth_cd": { "title": "Net financial flows, others (NFL, current US$)", "description_from_producer": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Others is a residual category in the World Bank's Debtor Reporting System. It includes such institutions as the Caribbean Development Fund, Council of Europe, European Development Fund, Islamic Development Bank, Nordic Development Fund, and the like. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.MOTH.CD (https://data.worldbank.org/indicator/DT.NFL.MOTH.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.MOTH.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_nifc_cd": { "title": "IFC, private nonguaranteed (NFL, US$)", "description_from_producer": "Nonguaranteed long-term debt privately placed from the International Finance Corporation (IFC). Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.NIFC.CD (https://data.worldbank.org/indicator/DT.NFL.NIFC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.NIFC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_offt_cd": { "title": "PPG, official creditors (NFL, US$)", "description_from_producer": "Public and publicly guaranteed debt from official creditors includes loans from international organizations (multilateral loans) and loans from governments (bilateral loans). Loans from international organization include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Government loans include loans from governments and their agencies (including central banks), loans from autonomous bodies, and direct loans from official export credit agencies. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.OFFT.CD (https://data.worldbank.org/indicator/DT.NFL.OFFT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.OFFT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_pbnd_cd": { "title": "PPG, bonds (NFL, current US$)", "description_from_producer": "Public and publicly guaranteed debt from bonds that are either publicly issued or privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.PBND.CD (https://data.worldbank.org/indicator/DT.NFL.PBND.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.PBND.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_pcbk_cd": { "title": "PPG, commercial banks (NFL, current US$)", "description_from_producer": "Public and publicly guaranteed commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.PCBK.CD (https://data.worldbank.org/indicator/DT.NFL.PCBK.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.PCBK.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_pcbo_cd": { "title": "Commercial banks and other lending (PPG + PNG) (NFL, current US$)", "description_from_producer": "Commercial bank and other lending includes net commercial bank lending (public and publicly guaranteed and private non- guaranteed) and other private credits. Data are in current U.S. dollars.\n\nProposed: Commercial bank and other lending includes net commercial bank and other private creditors lending excluding bonds (public and publicly guaranteed + private nonguaranteed).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Commercial banks include all commercial banks, whether or not publicly owned, that provide loans and other financial services. Private creditors include commercial banks, bondholders, and other private creditors. This indicator includes only publicly guaranteed creditors. Nonguaranteed private creditors are shown separately. Bonds include publicly issued or privately placed bonds. Commercial bank loans are loans from private banks and other private financial institutions. Credits of other private creditors include credits from manufacturers, exporters, and other suppliers of goods, plus bank credits covered by a guarantee of an export credit agency.\n\nChanged to:\nCountries that report to the World Bank\u2019s Debtor Reporting System (DRS) submit their 1- public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis and 2- private nonguaranteed (PNG) debt on an aggregate basis. The World Bank Debt Data Team then compiles this information to produce this indicator.\n\n\nStatistical concept(s): Commercial banks include all commercial banks, whether or not publicly owned, that provide loans and other financial services. Private creditors include commercial banks, bondholders, and other private creditors. This indicator includes only publicly guaranteed creditors. Nonguaranteed private creditors are shown separately. Bonds include publicly issued or privately placed bonds. Commercial bank loans are loans from private banks and other private financial institutions. Credits of other private creditors include credits from manufacturers, exporters, and other suppliers of goods, plus bank credits covered by a guarantee of an export credit agency.\n\nproposed: Commercial banks include all commercial banks, whether or not publicly owned, that provide loans and other financial services. Credits of other private creditors include credits from manufacturers, exporters, and other suppliers of goods, plus bank credits covered by a guarantee of an export credit agency.\n\n### Development relevance:\nExternal debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nProposed: External debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the governments, corporations or private households. The debt includes money owed to governments or public agencies (bilateral), to international organizations (multilateral) or to exporters, commercial banks or other financial institutions.\nExternal indebtedness affects a country's creditworthiness and investor perceptions. When used effectively, a reasonable level of external debt can help a country finance productive investments, such as building infrastructure and investing in education and health, that can increase growth.\n\n### Limitations and exceptions:\nData on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics. Indicator DT.NFL.PCBO.CD (https://data.worldbank.org/indicator/DT.NFL.PCBO.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.PCBO.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_pngb_cd": { "title": "PNG, bonds (NFL, current US$)", "description_from_producer": "Nonguaranteed long-term debt from bonds that are privately placed. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Countries that report to the World Bank\u2019s Debtor Reporting System (DRS) submit their private\u00a0nonguaranteed\u00a0(PNG) on an aggregate basis. The World Bank Debt Data Team then compiles this information to produce this indicator.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics, note: https://www.worldbank.org/en/programs/debt-statistics. Indicator DT.NFL.PNGB.CD (https://data.worldbank.org/indicator/DT.NFL.PNGB.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.PNGB.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_pngc_cd": { "title": "PNG, commercial banks and other creditors (NFL, current US$)", "description_from_producer": "Nonguaranteed long-term commercial bank loans from private banks and other private financial institutions. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Countries that report to the World Bank\u2019s Debtor Reporting System (DRS) submit their private\u00a0nonguaranteed\u00a0(PNG) on an aggregate basis. The World Bank Debt Data Team then compiles this information to produce this indicator.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics, note: https://www.worldbank.org/en/programs/debt-statistics. Indicator DT.NFL.PNGC.CD (https://data.worldbank.org/indicator/DT.NFL.PNGC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.PNGC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_prop_cd": { "title": "PPG, other private creditors (NFL, current US$)", "description_from_producer": "Public and publicly guaranteed other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.PROP.CD (https://data.worldbank.org/indicator/DT.NFL.PROP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.PROP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_prvt_cd": { "title": "PPG, private creditors (NFL, US$)", "description_from_producer": "Public and publicly guaranteed debt from private creditors include bonds that are either publicly issued or privately placed; commercial bank loans from private banks and other private financial institutions; and other private credits from manufacturers, exporters, and other suppliers of goods, and bank credits covered by a guarantee of an export credit agency. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.NFL.PRVT.CD (https://data.worldbank.org/indicator/DT.NFL.PRVT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.PRVT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_rdbc_cd": { "title": "Net financial flows, RDB concessional (NFL, current US$)", "description_from_producer": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Concessional financial flows cover disbursements made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Regional Development Banks (RDB) share their lending information on a loan by loan basis with the World Bank through the World Bank's Debtor Reporting System (DRS). The Debt Data Team then compiles this information to produce this indicator.\nStatistical concept(s): Regional development banks also maintain concessional windows. Their loans are recorded according to each institution's classification and not according to the Organisation for Economic Co-operation and Development's (OECD) Development Assistance Committee (DAC) definition.\n\n### Development relevance:\nConcessional finance is below market rate finance provided by major financial institutions, such as development banks and multilateral funds, to developing countries to accelerate development objectives. The term concessional finance does not represent a single mechanism or type of financial support but comprises a range of below market rate products used to accelerate development objective.\n\nConcessional finance often targets high-impact projects responding to globally significant development challenges \u2013 from climate change mitigation and resilience to vaccine deployment, water sanitation and education - that otherwise could not go ahead without specialized financial support.\n\nNet financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB), note: https://www.worldbank.org/en/programs/debt-statistics. Indicator DT.NFL.RDBC.CD (https://data.worldbank.org/indicator/DT.NFL.RDBC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.RDBC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_rdbn_cd": { "title": "Net financial flows, RDB nonconcessional (NFL, current US$)", "description_from_producer": "Net financial flows received by the borrower during the year are disbursements of loans and credits less repayments of principal. Nonconcessional financial flows cover all disbursements except those made through concessional lending facilities. Regional development banks are the African Development Bank, in Tunis, Tunisia, which serves all of Africa, including North Africa; the Asian Development Bank, in Manila, Philippines, which serves South and Central Asia and East Asia and Pacific; the European Bank for Reconstruction and Development, in London, United Kingdom, which serves Europe and Central Asia; and the Inter-American Development Bank, in Washington, D.C., which serves the Americas. Aggregates include amounts for economies not specified elsewhere. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Regional Development Banks (RDB) share their lending information on a loan by loan basis with the World Bank through the World Bank's Debtor Reporting System (DRS). The Debt Data Team then compiles this information to produce this indicator.\nStatistical concept(s): Regional development banks also maintain concessional windows. Their loans are recorded according to each institution's classification and not according to the Organisation for Economic Co-operation and Development's (OECD) Development Assistance Committee (DAC) definition.\n\n### Development relevance:\nThe financial flows from regional development banks (RDB) that are not made through concessional lending facilities. Concessional finance is below market rate finance provided by major financial institutions.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics. Indicator DT.NFL.RDBN.CD (https://data.worldbank.org/indicator/DT.NFL.RDBN.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.RDBN.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "NFL, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "NFL, current US$" } }, "dt_nfl_sdgf_cd": { "title": "Net official flows from UN agencies, SDGFUND (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United Nations Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.SDGF.CD (https://data.worldbank.org/indicator/DT.NFL.SDGF.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.SDGF.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_sprp_cd": { "title": "Net official flows from UN agencies, SPRP (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United Nations Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.SPRP.CD (https://data.worldbank.org/indicator/DT.NFL.SPRP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.SPRP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_unai_cd": { "title": "Net official flows from UN agencies, UNAIDS (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies include the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNAI.CD (https://data.worldbank.org/indicator/DT.NFL.UNAI.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNAI.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_uncd_cd": { "title": "Net official flows from UN agencies, UNCDF (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies include the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNCD.CD (https://data.worldbank.org/indicator/DT.NFL.UNCD.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNCD.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_uncf_cd": { "title": "Net official flows from UN agencies, UNICEF (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNCF.CD (https://data.worldbank.org/indicator/DT.NFL.UNCF.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNCF.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_uncr_cd": { "title": "Net official flows from UN agencies, UNHCR (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNCR.CD (https://data.worldbank.org/indicator/DT.NFL.UNCR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNCR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_unctad_cd": { "title": "Net official flows from UN agencies, UNCTAD (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United Nations Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNCTAD.CD (https://data.worldbank.org/indicator/DT.NFL.UNCTAD.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNCTAD.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_uncv_cd": { "title": "Net official flows from UN agencies, UNCOVID (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United Nations Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNCV.CD (https://data.worldbank.org/indicator/DT.NFL.UNCV.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNCV.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_undp_cd": { "title": "Net official flows from UN agencies, UNDP (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNDP.CD (https://data.worldbank.org/indicator/DT.NFL.UNDP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNDP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_unec_cd": { "title": "Net official flows from UN agencies, UNECE (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nData for net official flows from UNECE at present are reported at the regional level only. A more detailed breakdown by recipient country will be available in the future. For aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNEC.CD (https://data.worldbank.org/indicator/DT.NFL.UNEC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNEC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_unep_cd": { "title": "Net official flows from UN agencies, UNEP (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNEP.CD (https://data.worldbank.org/indicator/DT.NFL.UNEP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNEP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_unfp_cd": { "title": "Net official flows from UN agencies, UNFPA (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNFP.CD (https://data.worldbank.org/indicator/DT.NFL.UNFP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNFP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_unid_cd": { "title": "Net official flows from UN agencies, UNIDIR (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies include the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme (UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNID.CD (https://data.worldbank.org/indicator/DT.NFL.UNID.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNID.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_unido_cd": { "title": "Net official flows from UN agencies, UNIDO (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United National Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNIDO.CD (https://data.worldbank.org/indicator/DT.NFL.UNIDO.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNIDO.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_unpb_cd": { "title": "Net official flows from UN agencies, UNPBF (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies include the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme (UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNPB.CD (https://data.worldbank.org/indicator/DT.NFL.UNPB.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNPB.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_unrw_cd": { "title": "Net official flows from UN agencies, UNRWA (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies include the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme (UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNRW.CD (https://data.worldbank.org/indicator/DT.NFL.UNRW.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNRW.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_unta_cd": { "title": "Net official flows from UN agencies, UNTA (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNTA.CD (https://data.worldbank.org/indicator/DT.NFL.UNTA.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNTA.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_unwn_cd": { "title": "Net official flows from UN agencies, UNWOMEN (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United Nations Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNWN.CD (https://data.worldbank.org/indicator/DT.NFL.UNWN.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNWN.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_unwt_cd": { "title": "Net official flows from UN agencies, UNWTO (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies include the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme (UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.UNWT.CD (https://data.worldbank.org/indicator/DT.NFL.UNWT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.UNWT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_nfl_wfpg_cd": { "title": "Net official flows from UN agencies, WFP (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.WFPG.CD (https://data.worldbank.org/indicator/DT.NFL.WFPG.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.WFPG.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_whol_cd": { "title": "Net official flows from UN agencies, WHO (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children\u2019s Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO) and United Nations Institute for Disarmament Research (UNIDIR). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.WHOL.CD (https://data.worldbank.org/indicator/DT.NFL.WHOL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.WHOL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_nfl_witc_cd": { "title": "Net official flows from UN agencies, WTO-ITC (current US$)", "description_from_producer": "Net official flows from UN agencies are the net disbursements of total official flows from the UN agencies. Total official flows are the sum of Official Development Assistance (ODA) or official aid and Other Official Flows (OOF) and represent the total disbursements by the official sector at large to the recipient country. Net disbursements are gross disbursements of grants and loans minus repayments of principal on earlier loans. ODA consists of loans made on concessional terms (with a grant element of at least 25 percent, calculated at a rate of discount of 10 percent) and grants made to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Official aid refers to aid flows from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. OOF are transactions by the official sector whose main objective is other than development-motivated, or, if development-motivated, whose grant element is below the 25 per cent threshold which would make them eligible to be recorded as ODA. The main classes of transactions included here are official export credits, official sector equity and portfolio investment, and debt reorganization undertaken by the official sector at nonconcessional terms (irrespective of the nature or the identity of the original creditor). UN agencies are United Nations includes the United Nations Children's Fund (UNICEF), United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), World Food Programme (WFP), International Fund for Agricultural Development (IFAD), United Nations Development Programme(UNDP), United Nations Population Fund (UNFPA), United Nations Refugee Agency (UNHCR), Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Regular Programme for Technical Assistance (UNTA), United Nations Peacebuilding Fund (UNPBF), International Atomic Energy Agency (IAEA), World Health Organization (WHO), United Nations Economic Commission for Europe (UNECE), Food and Agriculture Organization of the United Nations (FAO), International Labour Organization (ILO), United Nations Environment Programme (UNEP), World Tourism Organization (UNWTO), United Nations Institute for Disarmament Research (UNIDIR), United Nations Capital Development Fund (UNCDF), WHO-Strategic Preparedness and Response Plan (SPRP), United Nations Women (UNWOMEN), Covid-19 Response and Recovery Multi-Partner Trust Fund (UNCOVID), Joint Sustainable Development Goals Fund (SDGFUND), Central Emergency Response Fund (CERF), WTO-International Trade Centre (WTO-ITC), United Nations Conference on Trade and Development (UNCTAD), and United Nations Industrial Development Organization (UNIDO). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Official flows from UN agencies indicate the net flow of funds. To calculate the net amount, the total disbursed during a specific accounting period is summed, and then any loan principal repayments (excluding interest) are subtracted. Disbursements represent the actual international transfer of financial resources, goods, or services at the donor's expense, occurring when funds are transferred to the service provider or recipient. For activities conducted within donor countries, such as training, disbursement is recorded once the funds are transferred. The data is expressed in U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.NFL.WITC.CD (https://data.worldbank.org/indicator/DT.NFL.WITC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.NFL.WITC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_oda_alld_cd": { "title": "Net official development assistance and official aid received (current US$)", "description_from_producer": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions).\n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nNet official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.ODA.ALLD.CD (https://data.worldbank.org/indicator/DT.ODA.ALLD.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.ODA.ALLD.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_oda_alld_kd": { "title": "Net official development assistance and official aid received (constant 2023 US$)", "description_from_producer": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in constant 2023 U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions).\n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in constant U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nNet official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.ODA.ALLD.KD (https://data.worldbank.org/indicator/DT.ODA.ALLD.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.ODA.ALLD.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2023 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2023 US$" } }, "dt_oda_oatl_cd": { "title": "Net official aid received (current US$)", "description_from_producer": "Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in current U.S. dollars.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions).\n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nNet official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Data are in current U.S. dollars.\n\n### Development relevance:\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will.\n\n### Limitations and exceptions:\nData on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe nominal values may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.ODA.OATL.CD (https://data.worldbank.org/indicator/DT.ODA.OATL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.ODA.OATL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_oda_oatl_kd": { "title": "Net official aid received (constant 2023 US$)", "description_from_producer": "Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in constant 2023 U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Net official aid refers to aid flows (net of repayments) from official donors to countries and territories in part II of the DAC list of recipients: more advanced countries of Central and Eastern Europe, the countries of the former Soviet Union, and certain advanced developing countries and territories. Official aid is provided under terms and conditions similar to those for ODA. Part II of the DAC List was abolished in 2005. The collection of data on official aid and other resource flows to Part II countries ended with 2004 data. Data are in constant U.S. dollars.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.ODA.OATL.KD (https://data.worldbank.org/indicator/DT.ODA.OATL.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.ODA.OATL.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_oda_odat_cd": { "title": "Net official development assistance received (current US$)", "description_from_producer": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions).\n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in current U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database.\n\n### Development relevance:\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about USD 130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will.\n\n### Limitations and exceptions:\nData on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe nominal values may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.ODA.ODAT.CD (https://data.worldbank.org/indicator/DT.ODA.ODAT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.ODA.ODAT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_oda_odat_gi_zs": { "title": "Net ODA received (% of gross capital formation)", "description_from_producer": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).Gross capital formation consists of outlays on additions to the economy's fixed assets plus net changes in the level of inventories. It is generally obtained from industry reports of acquisitions and distinguishes only the broad categories of capital formation. Data on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow methods using data from production, trade and construction activities.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of gross capital formation is calculated using values in U.S. dollars converted at official exchange rates.\n\n### Development relevance:\nThe ratio of aid to gross capital formation provides a measure of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, particularly in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980s, along with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (gross capital formation).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will.\n\n### Limitations and exceptions:\nData on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to gross capital formation provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs. The quality of data on government fixed capital formation depends on the quality of government accounting systems which tend to be weak in developing countries. Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable. Estimates of changes in inventories are rarely complete but usually include the most important activities of commodities.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD.", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank gross capital formation estimates, World Bank (WB), note: World Bank gross capital formation estimates are used for the denominator. Indicator DT.ODA.ODAT.GI.ZS (https://data.worldbank.org/indicator/DT.ODA.ODAT.GI.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.ODA.ODAT.GI.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of gross capital formation", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of gross capital formation" } }, "dt_oda_odat_gn_zs": { "title": "Net ODA received (% of GNI)", "description_from_producer": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of GNI is calculated using values in U.S. dollars converted at official exchange rates.\n\n### Development relevance:\nThe ratio of aid to GNI provides a measure of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, and they increased in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980s, along with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (GNI).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will.\n\n### Limitations and exceptions:\nData on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to gross national income (GNI) provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD.", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank GNI estimates, World Bank (WB), note: World Bank GNI estimates are used for the denominator. Indicator DT.ODA.ODAT.GN.ZS (https://data.worldbank.org/indicator/DT.ODA.ODAT.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.ODA.ODAT.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "dt_oda_odat_kd": { "title": "Net official development assistance received (constant 2023 US$)", "description_from_producer": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in constant 2023 U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on donor reports on bilateral programs by DAC members using standard questionnaires issued by the DAC Secretariat. DAC has 24 members - 23 individual economies and 1 multilateral institution (European Union institutions).\n\nNet official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of DAC, by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent). Data are in constant U.S. dollars.\n\nTotal net disbursements is the sum of grants, capital subscriptions (deposit basis), recoveries and total net loans and other long-term capital.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: https://stats.oecd.org/. Indicator DT.ODA.ODAT.KD (https://data.worldbank.org/indicator/DT.ODA.ODAT.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.ODA.ODAT.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "dt_oda_odat_mp_zs": { "title": "Net ODA received (% of imports of goods, services and primary income)", "description_from_producer": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nData on imports are compiled from customs reports and balance of payments data. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services. They exclude compensation of employees and investment income (factor services in the 1969 SNA) and transfer payments.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of imports of goods and services is calculated using values in U.S. dollars converted at official exchange rates.\n\n### Development relevance:\nThe ratio of aid to imports of goods and services provides a measure of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, and they increased in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980s, along with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (imports of goods and services).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will.\n\n### Limitations and exceptions:\nData on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to imports of goods and services provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs. Data on imports are compiled from customs reports and balance of payments data. Although data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or correspond to the change of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in league but unreported shuttle trade may further distort trade statistics.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD.", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank imports of good and services estimates, World Bank (WB), note: World Bank imports of good and services estimates are used for the denominator. Indicator DT.ODA.ODAT.MP.ZS (https://data.worldbank.org/indicator/DT.ODA.ODAT.MP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.ODA.ODAT.MP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of imports of goods, services and primary income", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of imports of goods, services and primary income" } }, "dt_oda_odat_pc_zs": { "title": "Net ODA received per capita (current US$)", "description_from_producer": "Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients; and is calculated by dividing net ODA received by the midyear population estimate. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nTotal population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship - except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. The values shown are midyear estimates. Net official development assistance per capita is net ODA divided by midyear population.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThis definition excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance (ODA) estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database.\n\n### Development relevance:\nThe ratio of aid per capita provides a measure of recipient country's dependency on aid. DAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about USD 130 billion. This demonstrates how effective aid pledges can be when they are made on the basis of adequate resources and backed by strong political will.\n\n### Limitations and exceptions:\nData on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD.", "origins": [ { "producer": "Development Assistance Committee - OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nWorld Bank population estimates, World Bank (WB), note: World Bank population estimates are used for the denominator. Indicator DT.ODA.ODAT.PC.ZS (https://data.worldbank.org/indicator/DT.ODA.ODAT.PC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.ODA.ODAT.PC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "dt_oda_odat_xp_zs": { "title": "Net ODA received (% of central government expense)", "description_from_producer": "Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\n### Statistical concept and methodology:\nMethodology: Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).\n\nCentral government expense is cash payments for operating activities of the government in providing goods and services. It includes compensation of employees (such as wages and salaries), interest and subsidies, grants, social benefits, and other expenses such as rent and dividends.\n\nThe flows of official and private financial resources from the members of the Development Assistance Committee (DAC) of the Organisation for Economic Co-operation and Development (OECD) to developing economies are compiled by DAC, based principally on reporting by DAC members using standard questionnaires issued by the DAC Secretariat.\n\nThe ODA excludes nonconcessional flows from official creditors, which are classified as \"other official flows,\" and aid for military and anti-terrorism purposes. Transfer payments to private individuals, such as pensions, reparations, and insurance payouts, are in general not counted. In addition to financial flows, ODA includes technical cooperation, most expenditures for peacekeeping under UN mandates and assistance to refugees, contributions to multilateral institutions such as the United Nations and its specialized agencies, and concessional funding to multilateral development banks.\n\nFlows are transfers of resources, either in cash or in the form of commodities or services measured on a cash basis. Short-term capital transactions (with one year or less maturity) are not counted. Repayments of the principal (but not interest) of ODA loans are recorded as negative flows. Proceeds from official equity investments in a developing country are reported as ODA, while proceeds from their later sale are recorded as negative flows.\n\nThe official development assistance estimates are published annually at the end of the calendar year in International Development Statistics (IDS) database. Net ODA received as a percent of central government expense is calculated using values in U.S. dollars converted using the DEC alternative conversion factor which is the underlying annual exchange rate used for the World Bank Atlas method.\n\n### Development relevance:\nRatio of aid to central government expense provides measures of recipient country's dependency on aid. Ratios of aid are generally much higher in Sub-Saharan Africa than in other regions, and they increased in the 1980s. High ratios are due only in part to aid flows. Many African countries saw severe erosion in their terms of trade in the 1980salong with weak policies, falling incomes, imports, and investment. Thus the increase in aid dependency ratios reflects events affecting both the numerator (aid) and the denominator (central government expense).\n\nDAC exists to help its members coordinate their development assistance and to encourage the expansion and improve the effectiveness of the aggregate resources flowing to recipient economies. In this capacity DAC monitors the flow of all financial resources, but its main concern is official development assistance (ODA). Grants or loans to countries and territories on the DAC list of aid recipients have to meet three criteria to be counted as ODA. They are provided by official agencies, including state and local governments, or by their executive agencies. They promote economic development and welfare as the main objective. And they are provided on concessional financial terms (loans must have a grant element of at least 25 percent, calculated at a discount rate of 10 percent). The DAC Statistical Reporting Directives provide the most detailed explanation of this definition and all ODA-related rules.\n\nDAC statistics aim to meet the needs of policy makers in the field of development co-operation, and to provide a means of assessing the comparative performance of aid donors. DAC statistics are used extensively in the Peer Reviews conducted for each DAC member every four to five years, and have a wide range of other applications. They are used to measure donors' compliance with various international recommendations in the field of development co-operation (terms, volume), and are indispensable for analysis of virtually every aspect of development and development co-operation.\n\nFrom 1960 to 1990, official development assistance (ODA) flows from DAC countries to developing countries rose steadily, but then fell sharply in the 1990s. Since then, a series of high-profile international conferences have boosted ODA flows. In the mid-2000s, ODA once again rose due to exceptional debt relief operations for Iraq and Nigeria. Despite the recent financial crisis, ODA flows have continued to rise and in the early 2010s reached their highest real level ever at about US $130 billion. This demonstrates effectiveness of aid pledges, especially when they are made on the basis of adequate resources and backed by strong political will.\n\n### Limitations and exceptions:\nData on ODA is for aid-receiving countries. The data cover loans and grants from DAC member countries, multilateral organizations, and non-DAC donors. They do not reflect aid given by recipient countries to other developing countries. As a result, some countries that are net donors are shown as aid recipients. The indicator does not distinguish types of aid (program, project, or food aid; emergency assistance; or post-conflict peacekeeping assistance), which may have different effects on the economy.\n\nRatio of aid to central government expense provides measures of recipient country's dependency on aid. But care must be taken in drawing policy conclusions. For foreign policy reasons some countries have traditionally received large amounts of aid. Thus aid dependency ratio may reveal as much about a donor's interests as about a recipient's needs.\n\nThe nominal values used here may overstate the real value of aid to recipients. Changes in international prices and exchange rates can reduce the purchasing power of aid. Tying aid, still prevalent though declining in importance, also tends to reduce its purchasing power. Tying requires recipients to purchase goods and services from the donor country or from a specified group of countries. Such arrangements prevent a recipient from misappropriating or mismanaging aid receipts, but they may also be motivated by a desire to benefit donor country suppliers.\n\nBecause the indicator relies on information from donors, it is not necessarily consistent with information recorded by recipients in the balance of payments, which often excludes all or some technical assistance - particularly payments to expatriates made directly by the donor. Similarly, grant commodity aid may not always be recorded in trade data or in the balance of payments. Moreover, DAC statistics exclude aid for military and antiterrorism purposes.\n\nThe aggregates refer to World Bank classifications of economies and therefore may differ from those of the OECD.", "origins": [ { "producer": "Development Assistance Committee - OECD and International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Development Assistance Committee, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nGeographical Distribution of Financial Flows to Developing Countries, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nDevelopment Co-operation Report, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nInternational Development Statistics database, Organisation for Economic Co-operation and Development (OECD), uri: www.oecd.org/dac/stats/idsonline;\nIMF central government expense estimates, International Monetary Fund (IMF), note: IMF central government expense estimates are used for the denominator. Indicator DT.ODA.ODAT.XP.ZS (https://data.worldbank.org/indicator/DT.ODA.ODAT.XP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.ODA.ODAT.XP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of central government expense", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of central government expense" } }, "dt_tds_dect_cd": { "title": "Debt service on external debt, total (TDS, current US$)", "description_from_producer": "Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the governments, corporations or private households. The debt includes money owed to governments or public agencies (bilateral), to international organizations (multilateral) or to exporters, commercial banks or other financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. When used effectively, a reasonable level of external debt can help a country finance productive investments, such as building infrastructure and investing in education and health, that can increase growth.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.TDS.DECT.CD (https://data.worldbank.org/indicator/DT.TDS.DECT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.TDS.DECT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "TDS, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "TDS, current US$" } }, "dt_tds_dect_ex_zs": { "title": "Total debt service (% of exports of goods, services and primary income)", "description_from_producer": "Total debt service to exports of goods, services and primary income. Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Development relevance:\nExternal debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the government, corporations or private households. The debt includes money owed to private commercial banks, other governments, or international financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. Nonreporting countries might have outstanding debt with the World Bank, other international financial institutions, or private creditors. Total debt service is contrasted with countries' ability to obtain foreign exchange through exports of goods, services, primary income, and workers' remittances.\n\nDebt ratios are used to assess the sustainability of a country's debt service obligations, but no absolute rules determine what values are too high. Empirical analysis of developing countries' experience and debt service performance shows that debt service difficulties become increasingly likely when the present value of debt reaches 200 percent of exports. Still, what constitutes a sustainable debt burden varies by country. Countries with fast-growing economies and exports are likely to be able to sustain higher debt levels. Various indicators determine a sustainable level of external debt, including:\n\na) debt to GDP ratio\nb) foreign debt to exports ratio\nc) government debt to current fiscal revenue ratio\nd) share of foreign debt\ne) short-term debt\nf) concessional debt in the total debt stock\n\n### Other notes:\nThe denominator for this indicator in previous versions of Global Development Finance included workers' remittances. Workers' remittances are no longer included.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.TDS.DECT.EX.ZS (https://data.worldbank.org/indicator/DT.TDS.DECT.EX.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.TDS.DECT.EX.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of exports of goods, services and primary income", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of exports of goods, services and primary income" } }, "dt_tds_dect_gn_zs": { "title": "Total debt service (% of GNI)", "description_from_producer": "Total debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term debt, interest paid on short-term debt, and repayments (repurchases and charges) to the IMF.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.TDS.DECT.GN.ZS (https://data.worldbank.org/indicator/DT.TDS.DECT.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.TDS.DECT.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "dt_tds_dimf_cd": { "title": "IMF repurchases and charges (TDS, current US$)", "description_from_producer": "IMF repurchases are total repayments of outstanding drawings from the General Resources Account during the year specified, excluding repayments due in the reserve tranche. IMF charges cover interest payments with respect to all uses of IMF resources, excluding those resulting from drawings in the reserve tranche. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.TDS.DIMF.CD (https://data.worldbank.org/indicator/DT.TDS.DIMF.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.TDS.DIMF.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "TDS, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "TDS, current US$" } }, "dt_tds_dppf_xp_zs": { "title": "Debt service (PPG and IMF only, % of exports of goods, services and primary income)", "description_from_producer": "Debt service, the sum of principal repayments and interest actually paid in currency, goods, or services, is expressed as a percentage of exports of goods and services--all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. This series differs from the standard debt to exports series in that it covers only long-term public and publicly guaranteed debt and repayments (repurchases and charges) to the IMF.\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.TDS.DPPF.XP.ZS (https://data.worldbank.org/indicator/DT.TDS.DPPF.XP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.TDS.DPPF.XP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "dt_tds_dppg_cd": { "title": "Debt service on external debt, public and publicly guaranteed (PPG) (TDS, current US$)", "description_from_producer": "Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Countries that report to the World Bank\u2019s Debtor Reporting System (DRS) submit their public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis. The World Bank Debt Data Team then compiles this information to produce this indicator.\n\n### Development relevance:\nExternal debt is that part of the total debt in a country that is owed to creditors outside the country. The debtors can be the governments, corporations or private households. The debt includes money owed to governments or public agencies (bilateral), to international organizations (multilateral) or to exporters, commercial banks or other financial institutions.\n\nExternal indebtedness affects a country's creditworthiness and investor perceptions. When used effectively, a reasonable level of external debt can help a country finance productive investments, such as building infrastructure and investing in education and health, that can increase growth.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.TDS.DPPG.CD (https://data.worldbank.org/indicator/DT.TDS.DPPG.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.TDS.DPPG.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "TDS, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "TDS, current US$" } }, "dt_tds_dppg_gn_zs": { "title": "Public and publicly guaranteed debt service (% of GNI)", "description_from_producer": "Public and publicly guaranteed debt service to gross national income. Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Countries that report to the World Bank\u2019s Debtor Reporting System (DRS) submit their public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis. The World Bank Debt Data Team then compiles this information to produce this indicator.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics. Indicator DT.TDS.DPPG.GN.ZS (https://data.worldbank.org/indicator/DT.TDS.DPPG.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.TDS.DPPG.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "dt_tds_dppg_xp_zs": { "title": "Public and publicly guaranteed debt service (% of exports of goods, services and primary income)", "description_from_producer": "Public and publicly guaranteed debt service to exports of goods, services, and income. Public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity. Exports of goods, services and primary income is the sum of goods (merchandise) exports, exports of (nonfactor) services and income (factor) receipts.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Countries that report to the World Bank\u2019s Debtor Reporting System (DRS) submit their public and publicly guaranteed debt (PPG) and private debt with a public guarantee on a loan-by-loan basis. The World Bank Debt Data Team then compiles this information to produce this indicator.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB), uri: https://www.worldbank.org/en/programs/debt-statistics. Indicator DT.TDS.DPPG.XP.ZS (https://data.worldbank.org/indicator/DT.TDS.DPPG.XP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.TDS.DPPG.XP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of exports of goods, services and primary income", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of exports of goods, services and primary income" } }, "dt_tds_mlat_cd": { "title": "Multilateral debt service (TDS, current US$)", "description_from_producer": "Public and publicly guaranteed multilateral loans include loans and credits from the World Bank, regional development banks, and other multilateral and intergovernmental agencies. Excluded are loans from funds administered by an international organization on behalf of a single donor government; these are classified as loans from governments. Debt service payments are the sum of principal repayments and interest payments actually made in the year specified. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.TDS.MLAT.CD (https://data.worldbank.org/indicator/DT.TDS.MLAT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.TDS.MLAT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "TDS, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "TDS, current US$" } }, "dt_tds_mlat_pg_zs": { "title": "Multilateral debt service (% of public and publicly guaranteed debt service)", "description_from_producer": "Multilateral debt service is the repayment of principal and interest to the World Bank, regional development banks, and other multilateral agencies. public and publicly guaranteed debt service is the sum of principal repayments and interest actually paid in currency, goods, or services on long-term obligations of public debtors and long-term private obligations guaranteed by a public entity.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on external debt are gathered through the World Bank's Debtor Reporting System (DRS). Long term debt data are compiled using the countries report on public and publicly guaranteed borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate basis. These data are supplemented by information from major multilateral banks and official lending agencies in major creditor countries. Short-term debt data are gathered from the Quarterly External Debt Statistics (QEDS) database, jointly developed by the World Bank and the IMF and from creditors through the reporting systems of the Bank for International Settlements. Debt data are reported in the currency of repayment and compiled and published in U.S. dollars. End-of-period exchange rates are used for the compilation of stock figures (amount of debt outstanding), and projected debt service and annual average exchange rates are used for the flows. Exchange rates are taken from the IMF's International Financial Statistics. Debt repayable in multiple currencies, goods, or services and debt with a provision for maintenance of the value of the currency of repayment are shown at book value.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator DT.TDS.MLAT.PG.ZS (https://data.worldbank.org/indicator/DT.TDS.MLAT.PG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/DT.TDS.MLAT.PG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of public and publicly guaranteed debt service", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of public and publicly guaranteed debt service" } }, "eg_cft_accs_ru_zs": { "title": "Access to clean fuels and technologies for cooking, rural (% of rural population)", "description_from_producer": "Access to clean fuels and technologies for cooking, rural is the proportion of rural population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization\u2019s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Trends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall \u2018polluting\u2019 fuels (unprocessed biomass, charcoal, coal, and kerosene) and \u2018clean\u2019 fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking.", "origins": [ { "producer": "International Energy Agency (IEA), International Renewable Energy Agency (IRENA), UN Statistics Division, World Health Organization (WHO), and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), note: License: Creative Commons Attribution\u2014NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO);\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report. Indicator EG.CFT.ACCS.RU.ZS (https://data.worldbank.org/indicator/EG.CFT.ACCS.RU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.CFT.ACCS.RU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "eg_cft_accs_ur_zs": { "title": "Access to clean fuels and technologies for cooking, urban (% of urban population)", "description_from_producer": "Access to clean fuels and technologies for cooking, urban is the proportion of urban population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization\u2019s (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities). Trends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall \u2018polluting\u2019 fuels (unprocessed biomass, charcoal, coal, and kerosene) and \u2018clean\u2019 fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking.", "origins": [ { "producer": "International Energy Agency (IEA), International Renewable Energy Agency (IRENA), UN Statistics Division, World Health Organization (WHO), and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), note: License: Creative Commons Attribution\u2014NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO);\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report. Indicator EG.CFT.ACCS.UR.ZS (https://data.worldbank.org/indicator/EG.CFT.ACCS.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.CFT.ACCS.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "eg_cft_accs_zs": { "title": "Access to clean fuels and technologies for cooking (% of population)", "description_short": "Access to [clean fuels or technologies](#dod:clean-cooking-fuels) such as natural gas, electricity, and clean cookstoves reduces exposure to indoor air pollutants, a leading cause of death in low-income households.", "description_from_producer": "Access to clean fuels and technologies for cooking is the proportion of total population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels.\n\nStatistical concept and methodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization's (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities).\n\nTrends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall \"polluting\" fuels (unprocessed biomass, charcoal, coal, and kerosene) and \"clean\" fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking.", "origins": [ { "producer": "Tracking SDG 7: The Energy Progress Report, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Tracking SDG 7: The Energy Progress Report, ACRA Europe, uri: https://trackingsdg7.esmap.org/, note: License: Creative Commons Attribution\u2014NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO), publisher: International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistical Division (UNSD), World Bank, World Health Organization (WHO), date published: 2025-06;\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report. Indicator EG.CFT.ACCS.ZS (https://data.worldbank.org/indicator/EG.CFT.ACCS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.CFT.ACCS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "name": "Access to clean fuels and technologies for cooking", "shortUnit": "%", "unit": "% of population" }, "processing_level": "minor", "presentation": { "title_public": "Share of the population with access to clean fuels for cooking", "attribution_short": "World Bank", "attribution": "World Health Organization (via World Bank)", "topic_tags": [ "Access to Energy", "Energy" ] } }, "eg_egy_prim_pp_kd": { "title": "Energy intensity level of primary energy (MJ/$2021 PPP GDP)", "description_from_producer": "Energy intensity level of primary energy is the ratio between energy supply and gross domestic product measured at purchasing power parity. Energy intensity is an indication of how much energy is used to produce one unit of economic output. Lower ratio indicates that less energy is used to produce one unit of output.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is obtained by dividing total primary energy supply over gross domestic product measured in constant 2021 US dollars at purchasing power parity.\n\n### Limitations and exceptions:\nEnergy intensity level is only an imperfect proxy to energy efficiency indicator and it can be affected by a number of factors not necessarily linked to pure efficiency such as climate.", "origins": [ { "producer": "International Energy Agency (IEA), International Renewable Energy Agency (IRENA), UN Statistics Division, World Health Organization (WHO), and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Tracking SDG 7: The Energy Progress Report, International Energy Agency (IEA), note: License: Creative Commons Attribution\u2014NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO);\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report. Indicator EG.EGY.PRIM.PP.KD (https://data.worldbank.org/indicator/EG.EGY.PRIM.PP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.EGY.PRIM.PP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "megajoules per international-$ in 2021 prices", "short_unit": "MJ/$", "display": { "shortUnit": "MJ/$", "unit": "megajoules per international-$ in 2021 prices" } }, "eg_elc_accs_ru_zs": { "title": "Access to electricity, rural (% of rural population)", "description_from_producer": "Access to electricity, rural is the percentage of rural population with access to electricity.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank\u2019s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021.\nStatistical concept(s):\n\n### Development relevance:\nMaintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indication of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.", "origins": [ { "producer": "SDG 7.1.1 Electrification Dataset, World Bank, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023. Indicator EG.ELC.ACCS.RU.ZS (https://data.worldbank.org/indicator/EG.ELC.ACCS.RU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.ACCS.RU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of rural population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of rural population" } }, "eg_elc_accs_ur_zs": { "title": "Access to electricity, urban (% of urban population)", "description_from_producer": "Access to electricity, urban is the percentage of urban population with access to electricity.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank\u2019s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021.\n\n### Development relevance:\nMaintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasingly dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indications of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.", "origins": [ { "producer": "SDG 7.1.1 Electrification Dataset, World Bank, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023. Indicator EG.ELC.ACCS.UR.ZS (https://data.worldbank.org/indicator/EG.ELC.ACCS.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.ACCS.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of urban population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of urban population" } }, "eg_elc_accs_zs": { "title": "Access to electricity (% of population)", "description_short": "Access to electricity means having an electricity source that can provide very basic lighting, and charge a phone or power a radio for 4 hours per day.", "description_from_producer": "Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank\u2019s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021.\n\n### Development relevance:\nMaintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasingly dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indications of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.", "description_key": "- Access to electricity improves people's living conditions in many ways. [Light at night](https://ourworldindata.org/light-at-night) makes it possible to get together after sunset; mobile phones allow us to stay in touch with those far away; refrigeration reduces food waste; and household appliances free up time from chores.\n- This data captures whether people have access to the most basic electricity supply \u2014 just enough to provide basic lighting and charge a phone or power a radio for 4 hours per day.\n- It shows that, especially in several African countries, a large share of the population lacks the benefits of basic electricity.\n- Universal access to electricity by 2030 is one of the United Nations [Sustainable Development Goals](https://ourworldindata.org/sdgs/affordable-clean-energy#sdg-indicator-7-1-1-access-to-electricity).\n- This data comes from the World Bank's World Development Indicators. Estimates are based on national household surveys, census data, and reports from energy providers or government agencies. Where data is missing, values are estimated by the source using a model based on trends across countries, regions, and time. Countries classified as \u201cdeveloped\u201d by the United Nations are assumed to have universal access.\n- To learn more, read our article: [Definitions: access to electricity](https://ourworldindata.org/definition-electricity-access).", "origins": [ { "producer": "SDG 7.1.1 Electrification Dataset, World Bank, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023. Indicator EG.ELC.ACCS.ZS (https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "numSignificantFigures": 2, "tolerance": 5, "shortUnit": "%", "unit": "% of population" }, "processing_level": "minor", "presentation": { "title_public": "Share of the population with access to electricity", "attribution_short": "World Bank", "attribution": "Data compiled from multiple sources by the World Bank", "topic_tags": [ "Access to Energy", "Energy" ] } }, "eg_elc_coal_zs": { "title": "Electricity production from coal sources (% of total)", "description_from_producer": "The share of electricity production from coal sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Coal refers to all coal and brown coal, both primary (including hard coal and lignite-brown coal) and derived fuels (including patent fuel, coke oven coke, gas coke, coke oven gas, and blast furnace gas). Peat is also included in this category.\n\n### Aggregation method:\nWeighted average using total electricity production as weights\n\n### Statistical concept and methodology:\nMethodology: Electricity production is total number of kilowatt-hours (kWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy.\n\n### Development relevance:\nSince the beginning of the 21st century, coal has been the fastest-growing global energy source; it currently provides about 40 percent of the world's electricity needs. Coal is the second source of primary energy in the world after oil, and the first source of electricity generation.. The last decade's growth in coal use has been driven by the economic growth of developing economies, mainly China. Irrespective of its economic benefits for the countries, the environmental impact of coal use, especially that coming from carbon dioxide emissions, is significant, and efforts are underway globally to build more efficient plants, to retrofit old plants and to decommission the oldest and least efficient coal plants.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products.\n\n### Limitations and exceptions:\nIEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable.\n\n### Other notes:\nElectricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.ELC.COAL.ZS (https://data.worldbank.org/indicator/EG.ELC.COAL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.COAL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "name": "Coal", "shortUnit": "%", "unit": "% of total" }, "presentation": { "title_public": "Share of total electricity production from coal" } }, "eg_elc_fosl_zs": { "title": "Electricity production from oil, gas and coal sources (% of total)", "description_from_producer": "The share of electricity production from oil, gas and coal sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Oil refers to crude oil and petroleum products. Gas refers to natural gas but excludes natural gas liquids. Coal refers to all coal and brown coal, both primary (including hard coal and lignite-brown coal) and derived fuels (including patent fuel, coke oven coke, gas coke, coke oven gas, and blast furnace gas). Peat is also included in this category.\n\n### Aggregation method:\nWeighted average using total electricity production as weights\n\n### Statistical concept and methodology:\nMethodology: Electricity production from oil, gas and coal sources (% of total) is the share of electricity produced by oil and petroleum products, natural gas, which is natural gas but not natural gas liquids, and coal in total electricity production which is the total number of Gigawatt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy.\n\n### Development relevance:\nOil, gas and coal, which are fossil fuels, account for well over 70% of the World's electricity generation. Irrespective of its economic benefits for the countries, the environmental impact of fossil fuels use, especially that coming from carbon dioxide emissions, is significant, and efforts are underway globally to build more efficient plants, to retrofit old plants and to decommission the oldest and least efficient plants as well as to transition to renewable energy sources.\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.ELC.FOSL.ZS (https://data.worldbank.org/indicator/EG.ELC.FOSL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.FOSL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "eg_elc_hyro_zs": { "title": "Electricity production from hydroelectric sources (% of total)", "description_from_producer": "The share of electricity production from hydroelectric sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Hydropower refers to electricity produced by hydroelectric power plants.\n\n### Aggregation method:\nWeighted average using total electricity production as weights\n\n### Statistical concept and methodology:\nMethodology: Electricity production is total number of kilowatt-hours (kWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy.\n\n### Development relevance:\nElectrical energy from hydropower is derived from turbines being driven by flowing water in rivers, with or without man-made dams forming reservoirs. Presently, hydropower is the world's largest source of renewable electricity. Hydropower represents the largest share of renewable electricity production. It was second only to wind power for new-built capacities between 2005 and 2010. IEA estimates that hydropower could produce up to 6,000 terawatt-hours in 2050, roughly twice as much as today.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHydropower's storage capacity and fast response characteristics are especially valuable to meet sudden fluctuations in electricity demand and to match supply from less flexible electricity sources and variable renewable sources, such as solar photovoltaic (PV) and wind power.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products.\n\n### Limitations and exceptions:\nIEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable.\n\n### Other notes:\nElectricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.ELC.HYRO.ZS (https://data.worldbank.org/indicator/EG.ELC.HYRO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.HYRO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "eg_elc_loss_zs": { "title": "Electric power transmission and distribution losses (% of output)", "description_from_producer": "Electric power transmission and distribution losses include losses in transmission between sources of supply and points of distribution and in the distribution to consumers, including pilferage. The losses are expressed as a share of the total output.\n\n### Aggregation method:\nWeighted average using total electricity production as weights\n\n### Statistical concept and methodology:\nMethodology: Data on electric power production and consumption are collected from national energy agencies by the International Energy Agency (IEA) and adjusted by the IEA to meet international definitions. Electric power transmission and distribution losses percentage of output is the share of electric power transmission and distribution losses to electricity production which is the total number of GWh generated by power plants separated into electricity plants and combined heat and power (CHP) plants.\n\n### Development relevance:\nAn economy's production and consumption of electricity are basic indicators of its size and level of development. Although a few countries export electric power, most production is for domestic consumption. Expanding the supply of electricity to meet the growing demand of increasingly urbanized and industrialized economies without incurring unacceptable social, economic, and environmental costs is one of the great challenges facing developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nModern societies are becoming increasing dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies. Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions.\n\n### Limitations and exceptions:\nElectricity consumption is equivalent to production less power plants' own use and transmission, distribution, and transformation losses less exports plus imports. It includes consumption by auxiliary stations, losses in transformers that are considered integral parts of those stations, and electricity produced by pumping installations. Where data are available, it covers electricity generated by primary sources of energy - coal, oil, gas, nuclear, hydro, geothermal, wind, tide and wave, and combustible renewables. Neither production nor consumption data capture the reliability of supplies, including breakdowns, load factors, and frequency of outages.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.ELC.LOSS.ZS (https://data.worldbank.org/indicator/EG.ELC.LOSS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.LOSS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of output", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of output" } }, "eg_elc_ngas_zs": { "title": "Electricity production from natural gas sources (% of total)", "description_from_producer": "The share of electricity production from natural gas sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Gas refers to natural gas but excludes natural gas liquids.\n\n### Aggregation method:\nWeighted average using total electricity production as weights\n\n### Statistical concept and methodology:\nMethodology: Electricity production from natural gas sources (% of total) is the share of natural gas, which is natural gas but not natural gas liquids, in total electricity production which is the total number of Gigawatt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy.\n\n### Development relevance:\nNatural gas is considered a good source of electricity supply for a number of economic, operational and environmental reasons, such as:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n1) it is technically and financially of low-risk;\n\n\n\n\n\n\n\n\n\n2) lower carbon relative to other fossil fuels;\n\n\n\n\n\n\n\n\n\n3) gas plants can be built relatively quickly in around two years, unlike nuclear facilities, which can take much longer.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAlso, gas plants are flexible both in technical and economic terms, so they can react quickly to demand peaks, and are ideally twinned with intermittent renewable options such as wind power.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products.\n\n### Limitations and exceptions:\nIEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable.\n\n### Other notes:\nElectricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.ELC.NGAS.ZS (https://data.worldbank.org/indicator/EG.ELC.NGAS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.NGAS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "eg_elc_nucl_zs": { "title": "Electricity production from nuclear sources (% of total)", "description_from_producer": "The share of electricity production from nuclear sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Nuclear power refers to electricity produced by nuclear power plants.\n\n### Aggregation method:\nWeighted average using total electricity production as weights\n\n### Statistical concept and methodology:\nMethodology: Electricity production from nuclear sources (% of total) is the share of electricity produced by nuclear power plants in total electricity production which is the total number of Gigawatt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy.\n\n### Development relevance:\nThe generation of electricity using nuclear energy was first demonstrated in the 1950s, and the first commercial nuclear power plants entered operation in the early 1960s. Nuclear capacity grew rapidly in the 1970s and 1980s as countries sought to reduce dependence on fossil fuels, especially after the oil crises of the 1970s. There was a renewed interest in nuclear energy from 2000, and 60 new countries expressed interest in launching a nuclear program to the International Atomic Energy Agency (IAEA). However, after the earthquake and tsunami devastation of the Pacific coast of northern Japan, most nuclear countries announced safety reviews of their nuclear reactors (stress tests) and the revision/improvement of their plans to address similar emergency situations; countries such as Germany and Italy decided to eventually phase out nuclear power or to abandon their nuclear plant projects.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products.\n\n### Limitations and exceptions:\nIEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable.\n\n### Other notes:\nElectricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.ELC.NUCL.ZS (https://data.worldbank.org/indicator/EG.ELC.NUCL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.NUCL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "eg_elc_petr_zs": { "title": "Electricity production from oil sources (% of total)", "description_from_producer": "The share of electricity production from oil sources of total electricity production. Sources of electricity refer to the inputs used to generate electricity. Oil refers to crude oil and petroleum products.\n\n### Aggregation method:\nWeighted average using total electricity production as weights\n\n### Statistical concept and methodology:\nMethodology: Electricity production from oil sources (% of total) is the share of electricity produced by oil and petroleum products in total electricity production which is the total number of Gigawatt-hours (GWh) generated by power plants separated into electricity plants and combined heat and power (CHP) plants. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy.\n\n### Development relevance:\nOil includes crude oil, condensates, natural gas liquids, refinery feedstocks and additives, other hydrocarbons (including emulsified oils, synthetic crude oil, mineral oils extracted from bituminous minerals such as oil shale, and bituminous sand) and petroleum products (refinery gas, ethane, LPG, aviation gasoline, motor gasoline, jet fuels, kerosene, gas/diesel oil, heavy fuel oil, naphtha, white spirit, lubricants, bitumen, paraffin waxes and petroleum coke).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAnthropogenic carbon dioxide emissions result primarily from fossil fuel combustion and cement manufacturing. In combustion different fossil fuels release different amounts of carbon dioxide for the same level of energy use: oil releases about 50 percent more carbon dioxide than natural gas, and coal releases about twice as much. Nuclear energy does not generate carbon dioxide emissions, but it produces other dangerous waste products.\n\n### Limitations and exceptions:\nIEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on access to electricity are collected by the IEA from industry, national surveys, and international sources.\n\n### Other notes:\nElectricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.ELC.PETR.ZS (https://data.worldbank.org/indicator/EG.ELC.PETR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.PETR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "eg_elc_rnew_zs": { "title": "Renewable electricity output (% of total electricity output)", "description_from_producer": "Renewable electricity is the share of electricity generated by renewable power plants in total electricity generated by all types of plants.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The share of renewable electricity output is calculated using the formula:\n\n\n\n\n\nRenewable\u00a0Electricity\u00a0Share(%)=(Electricity\u00a0from\u00a0Renewable\u00a0Sources\u00a0(MWh) / Total\u00a0Electricity\u00a0Output\u00a0(MWh))\u00d7100\n\n\n\n\n\nWhere:\n\n\n\n\n\nElectricity from Renewable Sources = Total electricity generated from hydropower, wind, solar, biomass, geothermal, and ocean energy.\n\n\n\n\n\nTotal Electricity Output = Sum of electricity generated from all sources, including fossil fuels, nuclear, and renewables.\n\n### Development relevance:\nRenewable energy sources are essential for reducing greenhouse gas emissions and combating climate change. They help decrease dependence on fossil fuels, enhancing energy security and price stability. The sector also drives economic growth by creating jobs and attracting investment. Technological advancements in renewables support innovation in storage, smart grids, and sustainable infrastructure. Additionally, they improve energy access in remote areas, promoting social and economic development worldwide.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.ELC.RNEW.ZS (https://data.worldbank.org/indicator/EG.ELC.RNEW.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.RNEW.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total electricity output", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total electricity output" } }, "eg_elc_rnwx_kh": { "title": "Electricity production from renewable sources, excluding hydroelectric (kWh)", "description_from_producer": "Electricity production from renewable sources in kilowatt-hour (kWh), excluding hydroelectric, includes geothermal, solar, tides, wind, biomass, and biofuels.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Electricity production from renewable sources in kilowatt-hour (kWh) is the amount of electricity produced by geothermal, solar photovoltaic, solar thermal, tide, wind, industrial waste, municipal waste, primary solid biofuels, biogases, biogasoline, biodiesels, other liquid biofuels, nonspecified primary biofuels and waste, and charcoal in total electricity production of both electricity plants and combined heat power (CHP) plants. Hydropower is excluded. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy.\n\n### Development relevance:\nRenewable energy sources are essential for reducing greenhouse gas emissions and combating climate change. They help decrease dependence on fossil fuels, enhancing energy security and price stability. The sector also drives economic growth by creating jobs and attracting investment. Technological advancements in renewables support innovation in storage, smart grids, and sustainable infrastructure. Additionally, they improve energy access in remote areas, promoting social and economic development worldwide.\n\n### Limitations and exceptions:\nIEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.ELC.RNWX.KH (https://data.worldbank.org/indicator/EG.ELC.RNWX.KH). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.RNWX.KH", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "kWh", "short_unit": "kWh", "display": { "shortUnit": "kWh", "unit": "kWh" } }, "eg_elc_rnwx_zs": { "title": "Electricity production from renewable sources, excluding hydroelectric (% of total)", "description_from_producer": "The share of electricity production from renewable sources of total electricity production. Electricity production from renewable sources, excluding hydroelectric, includes geothermal, solar, tides, wind, biomass, and biofuels.\n\n### Aggregation method:\nWeighted average using total electricity production as weights\n\n### Statistical concept and methodology:\nMethodology: Electricity production from renewable sources (% of total) is the share of electricity produced by geothermal, solar photovoltaic, solar thermal, tide, wind, industrial waste, municipal waste, primary solid biofuels, biogases, biogasoline, biodiesels, other liquid biofuels, nonspecified primary biofuels and waste, and charcoal in total electricity production which is the total number of Gigawatt-hours (GWh) generated by power plants separated into electricity plants and CHP plants. Hydropower is excluded. The International Energy Agency (IEA) compiles data on energy inputs used to generate electricity. IEA data for countries that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. In addition, estimates are sometimes made to complete major aggregates from which key data are missing, and adjustments are made to compensate for differences in definitions. The IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts.\nStatistical concept(s): Electricity production is the total amount of electricity generated by power plants in an economy.\n\n### Development relevance:\nRenewable energy sources are essential for reducing greenhouse gas emissions and combating climate change. They help decrease dependence on fossil fuels, enhancing energy security and price stability. The sector also drives economic growth by creating jobs and attracting investment. Technological advancements in renewables support innovation in storage, smart grids, and sustainable infrastructure. Additionally, they improve energy access in remote areas, promoting social and economic development worldwide.\n\n### Limitations and exceptions:\nIEA occasionally revises its time series to reflect political changes. For example, the IEA has constructed historical energy statistics for countries of the former Soviet Union. In addition, energy statistics for other countries have undergone continuous changes in coverage or methodology in recent years as more detailed energy accounts have become available. Breaks in series are therefore unavoidable.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.ELC.RNWX.ZS (https://data.worldbank.org/indicator/EG.ELC.RNWX.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.RNWX.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "eg_fec_rnew_zs": { "title": "Renewable energy consumption (% of total final energy consumption)", "description_from_producer": "Renewable energy consumption is the share of renewable energy in total final energy consumption.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The numerator includes the direct consumption of renewable energy sources plus the final consumption of gross electricity and heat estimated to have come from renewable sources, while the denominator is the total final energy consumption of all energy products.\nStatistical concept(s):", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.FEC.RNEW.ZS (https://data.worldbank.org/indicator/EG.FEC.RNEW.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.FEC.RNEW.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total final energy consumption", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total final energy consumption" } }, "eg_gdp_puse_ko_pp": { "title": "GDP per unit of energy use (PPP $ per kg of oil equivalent)", "description_from_producer": "GDP per unit of energy use is the PPP GDP per kilogram of oil equivalent of energy use. PPP GDP is gross domestic product converted to current international dollars using purchasing power parity rates based on the 2017 ICP round. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: GDP per unit of energy use, measured as PPP $ per kg of oil equivalent, is calculated by dividing the gross domestic product (PPP) by the total energy consumption, expressed in kilograms of oil equivalent.\n\n### Development relevance:\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.GDP.PUSE.KO.PP (https://data.worldbank.org/indicator/EG.GDP.PUSE.KO.PP). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.GDP.PUSE.KO.PP", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "PPP $ per kg of oil equivalent", "short_unit": "$", "display": { "shortUnit": "$", "unit": "PPP $ per kg of oil equivalent" } }, "eg_gdp_puse_ko_pp_kd": { "title": "GDP per unit of energy use (constant 2021 PPP $ per kg of oil equivalent)", "description_from_producer": "GDP per unit of energy use is the PPP GDP per kilogram of oil equivalent of energy use. PPP GDP is gross domestic product converted to 2021 constant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The ratio of gross domestic product (GDP) to energy use indicates energy efficiency. To produce comparable and consistent estimates of real GDP across economies relative to physical inputs to GDP - that is, units of energy use - GDP is converted to 2021 international dollars using purchasing power parity (PPP) rates. Differences in this ratio over time and across economies reflect structural changes in an economy, changes in sectoral energy efficiency, and differences in fuel mixes. Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. GDP data are from World Bank's national accounts files.\n\n### Development relevance:\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n### Limitations and exceptions:\nThe IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.GDP.PUSE.KO.PP.KD (https://data.worldbank.org/indicator/EG.GDP.PUSE.KO.PP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.GDP.PUSE.KO.PP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2021 PPP $ per kg of oil equivalent", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2021 PPP $ per kg of oil equivalent" } }, "eg_imp_cons_zs": { "title": "Energy imports, net (% of energy use)", "description_from_producer": "Net energy imports are estimated as gross imports less gross exports, both measured in tons of oil equivalents (toe). A negative value indicates that the country is a net exporter. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA negative value in energy imports indicates that the country is a net exporter. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport.\n\n### Development relevance:\nModern energy services are crucial to a country's economic development. Access to modern energy is essential for the provision of clean water, sanitation and healthcare and for the provision of reliable and efficient lighting, heating, cooking, mechanical power, and transport and telecommunications services.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions.\n\n### Limitations and exceptions:\nThe IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.IMP.CONS.ZS (https://data.worldbank.org/indicator/EG.IMP.CONS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.IMP.CONS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of energy use", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of energy use" } }, "eg_use_comm_cl_zs": { "title": "Alternative and nuclear energy (% of total energy use)", "description_from_producer": "Clean energy is noncarbohydrate energy that does not produce carbon dioxide when generated. It includes hydropower and nuclear, geothermal, and solar power, among others. This is the share of total energy supply that is non-fossil.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n### Development relevance:\nAlternative energy is produced without the undesirable consequences of the burning of fossil fuels, such as high carbon dioxide emissions, which is considered to be the major contributing factor of global warming.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPast few decade have seen a rise in global investment in renewable energy, led by wind and solar. In transport, major car companies are adding hybrid and full-electric vehicles to their product lines and many governments have launched plans to encourage consumers to buy these vehicles Fossil fuels continue to outpace alternative and renewable energy growth. Coal has been the fastest-growing global energy source, meeting about one-half of new electricity demand.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions.\n\n### Limitations and exceptions:\nThe IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.USE.COMM.CL.ZS (https://data.worldbank.org/indicator/EG.USE.COMM.CL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.USE.COMM.CL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total energy use", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total energy use" } }, "eg_use_comm_fo_zs": { "title": "Fossil fuel energy consumption (% of total)", "description_from_producer": "Fossil fuel comprises coal, oil, petroleum, and natural gas products.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power.\n\n### Development relevance:\nFossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity.\n\n### Limitations and exceptions:\nThe IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.USE.COMM.FO.ZS (https://data.worldbank.org/indicator/EG.USE.COMM.FO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.USE.COMM.FO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "eg_use_comm_gd_pp_kd": { "title": "Energy use (kg of oil equivalent) per $1,000 GDP (constant 2021 PPP)", "description_from_producer": "Energy use per PPP GDP is the kilogram of oil equivalent of energy use per constant PPP GDP. Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport. PPP GDP is gross domestic product converted to 2021 constant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a U.S. dollar has in the United States.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated by dividing the total energy use (in kg of oil equivalent) by the total GDP (in constant 2021 PPP dollars) and then multiplying by 1000, to express the energy use per $1,000 of GDP.\n\n### Development relevance:\n\"In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\nFossil fuels are non-renewable resources because they take millions of years to form, and reserves are being depleted much faster than new ones are being made. In developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\"", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.USE.COMM.GD.PP.KD (https://data.worldbank.org/indicator/EG.USE.COMM.GD.PP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.USE.COMM.GD.PP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2021 PPP", "display": { "unit": "constant 2021 PPP" } }, "eg_use_crnw_zs": { "title": "Combustible renewables and waste (% of total energy)", "description_from_producer": "Combustible renewables and waste comprise solid biomass, liquid biomass, biogas, industrial waste, and municipal waste, measured as a percentage of total energy use. The indicator expresses the share of total energy supply.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\nThe indicator is calculated as the share of biofuels and waste in the total energy supply of the country.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power.\n\n### Development relevance:\nTotal energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRenewable energy is derived from natural processes (e.g. sunlight and wind) that are replenished at a higher rate than they are consumed. Solar, wind, geothermal, hydro, and biomass are common sources of renewable energy. Majority of renewable energy in the world is from solid biofuels and hydroelectricity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nRenewable sources of energy have been the driver of much of the growth in the global clean energy sector in the past few decades. Recent years have seen a major scale-up of wind and solar photovoltaic (PV) technologies. Other renewable technologies - including hydropower, geothermal and biomass - continued to grow from a strong established base, adding hundreds of gigawatts of new capacity worldwide.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions.\n\n### Limitations and exceptions:\nThe IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.USE.CRNW.ZS (https://data.worldbank.org/indicator/EG.USE.CRNW.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.USE.CRNW.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total energy", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total energy" } }, "eg_use_elec_kh_pc": { "title": "Electric power consumption (kWh per capita)", "description_from_producer": "Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Electric power consumption per capita (kWh ) is the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants, divided by midyear population. Energy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments. Electricity consumption is equivalent to production less power plants' own use and transmission, distribution, and transformation losses less exports plus imports. It includes consumption by auxiliary stations, losses in transformers that are considered integral parts of those stations, and electricity produced by pumping installations. Where data are available, it covers electricity generated by primary sources of energy - coal, oil, gas, nuclear, hydro, geothermal, wind, tide and wave, and combustible renewables. Neither production nor consumption data capture the reliability of supplies, including breakdowns, load factors, and frequency of outages.\n\n### Development relevance:\nAn economy's production and consumption of electricity are basic indicators of its size and level of development. Although a few countries export electric power, most production is for domestic consumption. Expanding the supply of electricity to meet the growing demand of increasingly urbanized and industrialized economies without incurring unacceptable social, economic, and environmental costs is one of the great challenges facing developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nModern societies are becoming increasingly dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies. Maintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions.\n\n### Limitations and exceptions:\nData on electric power production and consumption are collected from national energy agencies by the International Energy Agency (IEA) and adjusted by the IEA to meet international definitions. Data are reported as net consumption as opposed to gross consumption. Net consumption excludes the energy consumed by the generating units. For all countries except the United States, total electric power consumption is equal to total net electricity generation plus electricity imports minus electricity exports minus electricity distribution losses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.USE.ELEC.KH.PC (https://data.worldbank.org/indicator/EG.USE.ELEC.KH.PC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.USE.ELEC.KH.PC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "kWh per capita", "short_unit": "kWh", "display": { "shortUnit": "kWh", "unit": "kWh per capita" } }, "eg_use_pcap_kg_oe": { "title": "Energy use (kg of oil equivalent per capita)", "description_from_producer": "Energy use refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Total energy use refers to the use of primary energy before transformation to other end-use fuels (such as electricity and refined petroleum products). It includes energy from combustible renewables and waste - solid biomass and animal products, gas and liquid from biomass, and industrial and municipal waste. Biomass is any plant matter used directly as fuel or converted into fuel, heat, or electricity. World Bank population estimates are used to calculate per capita data.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy data are compiled by the International Energy Agency (IEA). IEA data for economies that are not members of the Organisation for Economic Co-operation and Development (OECD) are based on national energy data adjusted to conform to annual questionnaires completed by OECD member governments.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData for combustible renewables and waste are often based on small surveys or other incomplete information and thus give only a broad impression of developments and are not strictly comparable across countries. The IEA reports include country notes that explain some of these differences. All forms of energy - primary energy and primary electricity - are converted into oil equivalents. A notional thermal efficiency of 33 percent is assumed for converting nuclear electricity into oil equivalents and 100 percent efficiency for converting hydroelectric power.\n\n### Development relevance:\nIn developing economies growth in energy use is closely related to growth in the modern sectors - industry, motorized transport, and urban areas - but energy use also reflects climatic, geographic, and economic factors (such as the relative price of energy). Energy use has been growing rapidly in low- and middle-income economies, but high-income economies still use almost five times as much energy on a per capita basis.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGovernments in many countries are increasingly aware of the urgent need to make better use of the world's energy resources. Improved energy efficiency is often the most economic and readily available means of improving energy security and reducing greenhouse gas emissions.\n\n### Limitations and exceptions:\nThe IEA makes these estimates in consultation with national statistical offices, oil companies, electric utilities, and national energy experts. The IEA occasionally revises its time series to reflect political changes, and energy statistics undergo continual changes in coverage or methodology as more detailed energy accounts become available. Breaks in series are therefore unavoidable.", "origins": [ { "producer": "IEA Energy Statistics Data Browser, IEA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25. Indicator EG.USE.PCAP.KG.OE (https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "kg of oil equivalent per capita", "short_unit": "kg of oil equivalent", "display": { "shortUnit": "kg of oil equivalent", "unit": "kg of oil equivalent per capita" } }, "en_atm_pm25_mc_m3": { "title": "PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)", "description_from_producer": "Population-weighted exposure to ambient PM2.5 pollution is defined as the average level of exposure of a nation's population to concentrations of suspended particles measuring less than 2.5 microns in aerodynamic diameter, which are capable of penetrating deep into the respiratory tract and causing severe health damage. Exposure is calculated by weighting mean annual concentrations of PM2.5 by population in both urban and rural areas.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population exposure to ambient PM2.5 air pollution is estimated using an integrated modeling approach developed for the Global Burden of Disease (GBD) study by the Institute for Health Metrics and Evaluation (IHME). Annual mean concentrations of fine particulate matter (PM2.5) are derived by combining satellite-based aerosol optical depth measurements, chemical transport models, and available ground-level air quality monitoring data. These data sources are fused using geophysical\u2013statistical models to generate globally consistent, high-resolution gridded estimates of PM2.5 concentrations, including areas without direct monitoring.\n\nPopulation exposure is calculated by weighting annual mean PM2.5 concentrations by the spatial distribution of population in both urban and rural areas. National estimates represent the population-weighted average annual concentration of PM2.5 to which a country\u2019s population is exposed. Estimates are produced annually using a consistent methodology to allow comparison across countries and over time. Values represent modeled exposure levels and are intended for comparative risk assessment rather than regulatory compliance monitoring.\nStatistical concept(s): Fine particulate matter (PM2.5) refers to airborne particles with an aerodynamic diameter of 2.5 micrometers or less, which are small enough to penetrate deeply into the human respiratory system. Exposure to PM2.5 is associated with adverse health outcomes, including cardiovascular and respiratory diseases and premature mortality.\n\n### Development relevance:\nAir pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world\u2019s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world\u2019s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly.\n\n### Limitations and exceptions:\nPollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households.", "origins": [ { "producer": "Global Burden of Disease Study 2023, Institute for Health Metrics and Evaluation (IHME), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Burden of Disease Study 2023 (GBD 2023) Air Pollution Exposure Estimates and Risk Curves 1990-2023, GBD Collaborator Network, uri: https://ghdx.healthdata.org/record/ihme-data/gbd-2023-air-pollution-exposure-estimates-1990-2023, note: Need to create account to retrieve data., publisher: Institute for Health Metrics and Evaluation (IHME), date accessed: 2026-04-03, date published: 2026-01-23. Indicator EN.ATM.PM25.MC.M3 (https://data.worldbank.org/indicator/EN.ATM.PM25.MC.M3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.ATM.PM25.MC.M3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "micrograms per cubic meter", "short_unit": "micrograms", "display": { "shortUnit": "micrograms", "unit": "micrograms per cubic meter" } }, "en_atm_pm25_mc_t1_zs": { "title": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-1 value (% of total)", "description_from_producer": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 1 (IT-1) is defined as the portion of a country\u2019s population living in places where mean annual concentrations of PM2.5 are greater than 35 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762\u20133772;GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990\u20132017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Pr\u00fcss-\u00dcst\u00fcn A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population.\n\n### Development relevance:\nAir pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world's people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world's people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exposure to PM. IT-1 level corresponds to the highest mean concentrations reported in studies of long-term effects, and may also reflect higher but unknown historical concentrations that may have been contributed to observed health effects. IT-1 level has been shown to be associated with significant mortality in the developed world.\n\n### Limitations and exceptions:\nPollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households.", "origins": [ { "producer": "Global Burden of Disease Study 2017 - Institute for Health Metrics and Evaluation (IHME), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Burden of Disease Study 2017 (GBD 2017), Institute for Health Metrics and Evaluation (IHME), uri: https://ghdx.healthdata.org/gbd-2017, publisher: Institute for Health Metrics and Evaluation (IHME), date published: 202112. Indicator EN.ATM.PM25.MC.T1.ZS (https://data.worldbank.org/indicator/EN.ATM.PM25.MC.T1.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.ATM.PM25.MC.T1.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "en_atm_pm25_mc_t2_zs": { "title": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-2 value (% of total)", "description_from_producer": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 2 (IT-2) is defined as the portion of a country\u2019s population living in places where mean annual concentrations of PM2.5 are greater than 25 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762\u20133772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990\u20132017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Pr\u00fcss-\u00dcst\u00fcn A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population.\nStatistical concept(s):\n\n### Development relevance:\nAir pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world\u2019s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world\u2019s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exporsure to PM. IT-2 level is greater than the mean concentration at which effects have been observed in studies of long-term exposure and mortality and is likely to be associated with significant health impacts from both long-term and daily exposures to PM2.5. Attainment of IT-2 value would reduce the health risks of long-term exposure by about 6% relative to the IT-1 value.\n\n### Limitations and exceptions:\nPollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households.", "origins": [ { "producer": "Brauer et al. (2017), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Brauer, M. et al. 2017, for the Global Burden of Disease Study 2017. Indicator EN.ATM.PM25.MC.T2.ZS (https://data.worldbank.org/indicator/EN.ATM.PM25.MC.T2.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.ATM.PM25.MC.T2.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "en_atm_pm25_mc_t3_zs": { "title": "PM2.5 pollution, population exposed to levels exceeding WHO Interim Target-3 value (% of total)", "description_from_producer": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the World Health Organization (WHO) Interim Target 3 (IT-3) is defined as the portion of a country\u2019s population living in places where mean annual concentrations of PM2.5 are greater than 15 micrograms per cubic meter. The Air Quality Guideline (AQG) of 10 micrograms per cubic meter is recommended by the WHO as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762\u20133772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990\u20132017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Pr\u00fcss-\u00dcst\u00fcn A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population.\n\n### Development relevance:\nAir pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world\u2019s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world\u2019s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly. Three interim targets were defined for PM2.5 and have been shown to be achievable with successive and sustained abatement measures. Countries may find these interim targets particularly helpful in gauging progress over time in the difficult process of steadily reducing population exporsure to PM. IT-3 level places greater weight than IT-2 on the likelihood of signifcant effects associated with long-term exposures. IT-3 value is close to the mean concentrations that are reported in studies of long-term exposure and provides an additional 6% reduction in mortality risk relative to the IT-2 value.\n\n### Limitations and exceptions:\nPollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households.", "origins": [ { "producer": "Global Burden of Disease Study 2017 - Institute for Health Metrics and Evaluation (IHME), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Burden of Disease Study 2017 (GBD 2017), Institute for Health Metrics and Evaluation (IHME), uri: https://ghdx.healthdata.org/gbd-2017, publisher: Institute for Health Metrics and Evaluation (IHME), date published: 202112. Indicator EN.ATM.PM25.MC.T3.ZS (https://data.worldbank.org/indicator/EN.ATM.PM25.MC.T3.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.ATM.PM25.MC.T3.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "en_atm_pm25_mc_zs": { "title": "PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total)", "description_from_producer": "Percent of population exposed to ambient concentrations of PM2.5 that exceed the WHO guideline value is defined as the portion of a country\u2019s population living in places where mean annual concentrations of PM2.5 are greater than 10 micrograms per cubic meter, the guideline value recommended by the World Health Organization as the lower end of the range of concentrations over which adverse health effects due to PM2.5 exposure have been observed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: A. van Donkelaar, R.V. Martin, M. Brauer, N.C. Hsu, R.A. Kahn, R.C. Levy, A. Lyapustin, A.M. Sayer, D.M. Winker, \"Global Estimates of Fine Particulate Matter using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors,\" Environ. Sci. Technol 50, no. 7 (2016): 3762\u20133772; GBD 2017 Risk Factors Collaborators, \"Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 194 countries and territories, 1990\u20132017: a systematic analysis for the Global Burden of Disease Study 2017,\" Lancet 392 (2018): 1923-1994; Shaddick G, Thomas M, Amini H, Broday DM, Cohen A, Frostad J, Green A, Gumy S, Liu Y, Martin RV, Pr\u00fcss-\u00dcst\u00fcn A, Simpson D, van Donkelaar A, Brauer M. Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environ Sci Technol. 2018 Jun 29. Data provided by Institute for Health Metrics and Evaluation, University of Washington, Seattle. Data on exposure to ambient air pollution are derived from estimates of annual concentrations of very fine particulates produced by the Global Burden of Disease study, an international scientific effort led by the Institute for Health Metrics and Evaluation at the University of Washington. Estimates of annual concentrations are generated by combining data from atmospheric chemistry transport models, satellite observations of aerosols in the atmosphere, and ground-level monitoring of particulates. Overlaying PM2.5 estimates with gridded population data, the percent of a nation's people that lives in areas where PM2.5 concentrations exceed recommended levels is calculated by summing the population for grid cells where PM2.5 concentrations are beyond a threshold value, in this case 10 micrograms per cubic meter, and then dividing by total population.\nStatistical concept(s):\n\n### Development relevance:\nAir pollution places a major burden on world health. In many places, including cities but also in rural areas, exposure to air pollution is the main environmental threat to health, responsible for 6.5 million deaths per year, about one every 5 seconds. Around 40 percent of the world\u2019s people rely on household burning of wood, charcoal, dung, crop waste, or coal to meet basic energy needs. Cooking and heating with solid fuels create harmful smoke and particles that fill homes and the surrounding environment. Household air pollution from cooking and heating with solid fuels is responsible for 2.9 million deaths a year. Long-term exposure to high levels of fine particles in the air contributes to a range of health effects, including respiratory diseases, lung cancer, and heart disease, resulting in 4.2 million deaths annually. Not only does exposure to air pollution affect the health of the world\u2019s people, it also carries huge economic costs and represents a drag on development, particularly for low and middle income countries and vulnerable segments of the population such as children and the elderly.\n\n### Limitations and exceptions:\nPollutant concentrations are sensitive to local conditions, and even monitoring sites in the same city may register different levels. Direct monitoring of PM2.5 is still rare in most parts of the world, and measurement protocols and standards are not the same for all countries. These data should be considered only a general indication of air quality, intended to inform cross-country comparisons of the health risks due to particulate matter pollution. The guideline set by the World Health Organization (WHO) for PM2.5 is that annual mean concentrations should not exceed 10 micrograms per cubic meter, representing the lower range over which adverse health effects have been observed. The WHO has also recommended guideline values for emissions of PM2.5 from burning fuels in households.", "origins": [ { "producer": "Global Burden of Disease Study 2017 - Institute for Health Metrics and Evaluation (IHME), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Burden of Disease Study 2017 (GBD 2017), Institute for Health Metrics and Evaluation (IHME), uri: https://ghdx.healthdata.org/gbd-2017, publisher: Institute for Health Metrics and Evaluation (IHME), date published: 202112. Indicator EN.ATM.PM25.MC.ZS (https://data.worldbank.org/indicator/EN.ATM.PM25.MC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.ATM.PM25.MC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "en_bir_thrd_no": { "title": "Bird species, threatened", "description_from_producer": "A threatened species is likely to become endangered within the foreseeable future, throughout all or a significant portion of its range. Threatened species are species classified by the IUCN as Critically Endangered (CR), Endangered (EN) and Vulnerable (VU).\n\nBirds are listed for countries included within their breeding or wintering ranges.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\n\n### Development relevance:\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The Red List Index for the world's birds shows that there has been a steady and continuing deterioration in the threat status of the world's birds since 1988, when the first complete global assessment was carried out.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGlobally, threatened birds occur worldwide - nearly all countries support one or more threatened bird species. Small islands hold disproportionately high numbers of Globally Threatened Birds, supporting over half of threatened species. Threatened seabirds are found throughout the world's oceans. The most important threats to the world's birds are the spread of agriculture and an ever increasing human use of biological resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future.\n\n### Limitations and exceptions:\nReporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List.", "origins": [ { "producer": "IUCN Red List (IUCN and UNEP-WCMC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The IUCN Red List of Threatened Species, UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.iucnredlist.org/;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.iucnredlist.org/. Indicator EN.BIR.THRD.NO (https://data.worldbank.org/indicator/EN.BIR.THRD.NO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.BIR.THRD.NO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_clc_drsk_xq": { "title": "Disaster risk reduction progress score (1-5 scale; 5=best)", "description_from_producer": "Disaster risk reduction progress score is an average of self-assessment scores, ranging from 1 to 5, submitted by countries under Priority 1 of the Hyogo Framework National Progress Reports. The Hyogo Framework is a global blueprint for disaster risk reduction efforts that was adopted by 168 countries in 2005. Assessments of \"Priority 1\" include four indicators that reflect the degree to which countries have prioritized disaster risk reduction and the strengthening of relevant institutions.\n\n### Statistical concept and methodology:\nMethodology: Resilience is measured by the disaster risk reduction progress score, an average of self-assessment scores submitted by countries under Priority 1 of the Hyogo Framework National Progress Reports. The Hyogo Framework is a global blueprint for disaster risk reduction efforts that was adopted by 168 countries in 2005. Assessments of Priority 1 include four indicators that reflect the degree to which countries have prioritized disaster risk reduction and the strengthening of relevant institutions.\nStatistical concept(s):\n\n### Development relevance:\nThe Hyogo Framework's goal is to substantially reduce disaster losses by 2015 - in lives, and in the social, economic, and environmental assets of communities and countries. The Hyogo Framework offers guiding principles, priorities for action, and practical means for achieving disaster resilience for vulnerable communities. Governments around the world have committed to take action to reduce disaster risk, and have adopted a guideline to reduce vulnerabilities to natural hazards, called the Hyogo Framework for Action (HFA). The HFA assists the efforts of nations and communities to become more resilient to, and cope better with the hazards that threaten their development gains.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nScientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems.\n\n### Limitations and exceptions:\nThe Hyogo Framework for Action (HFA) national progress reports assess strategic priorities in the implementation of disaster risk reduction actions and establish baselines on levels of progress achieved in implementing the HFA's five priorities for action. National reporting processes are led by officially designated HFA focal institutions in country, and regional reporting by regional intergovernmental organizations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHFA's five priorities are:\n\n\n\n\n\n\n\n1. Making disaster risk reduction a policy priority, institutional strengthening\n\n\n\n\n\n\n\n2. Risk assessment and early warning systems\n\n\n\n\n\n\n\n3. Education, information and public awareness\n\n\n\n\n\n\n\n4. Reducing underlying risk factors\n\n\n\n\n\n\n\n5. Preparedness for effective response", "origins": [ { "producer": "UN Office for Disaster Risk Reduction (UNDRR), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "2009-2011 Progress Reports, UN Office for Disaster Risk Reduction (UNDRR), uri: http://www.preventionweb.net/english/hyogo. Indicator EN.CLC.DRSK.XQ (https://data.worldbank.org/indicator/EN.CLC.DRSK.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.CLC.DRSK.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1-5 scale; 5=best", "display": { "unit": "1-5 scale; 5=best" } }, "en_clc_mdat_zs": { "title": "Droughts, floods, extreme temperatures (% of population, average 1990-2009)", "description_from_producer": "Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated.\n\n### Statistical concept and methodology:\nMethodology: This indicator measures vulnerability of population affected by droughts, floods, and extreme temperature. A drought is an extended period of deficiency in a region's water supply as a result of below average precipitation.\n\n### Development relevance:\nScientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA drought can lead to losses in agriculture, affect inland navigation and hydropower plants, reduce access to drinking water, and cause famines. A flood is a significant rise of water level in a stream, lake, reservoir, or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Accompanied by frost, it can damage agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes humid weather. Population affected by these natural disasters is the number of people injured, left homeless, or requiring immediate assistance and can include displaced or evacuated people.\n\n### Limitations and exceptions:\nThe 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is \"unequivocal\" and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20-86 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5\u00b0C by 2100 (Kattenberg and others 1996) and increase of \"killer\" heat waves during the warm season (Karl and others 1997).", "origins": [ { "producer": "EM-DAT (Centre for Research on the Epidemiology of Disasters - CRED), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EM-DAT The International Disaster Database, Centre for Research on the Epidemiology of Disasters (CRED) - Universit\u00e9 Catholique de Louvain, uri: https://www.emdat.be/. Indicator EN.CLC.MDAT.ZS (https://data.worldbank.org/indicator/EN.CLC.MDAT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.CLC.MDAT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population, average 1990-1990", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population, average 1990-1990" } }, "en_fsh_thrd_no": { "title": "Fish species, threatened", "description_from_producer": "A threatened species is likely to become endangered within the foreseeable future, throughout all or a significant portion of its range. Threatened species are species classified by the IUCN as Critically Endangered (CR), Endangered (EN) and Vulnerable (VU).\n\nFish species are based on Froese, R. and Pauly, D. (eds). 2008.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threatened species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\n\n### Development relevance:\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The Red List Index for the world's birds shows that there has been a steady and continuing deterioration in the threat status of the world's birds since 1988, when the first complete global assessment was carried out.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The introduction in 1994 of a scientifically rigorous approach to determine risks of extinction that is applicable to all species, has become a world standard. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe freshwater system represents the most threatened of all ecosystems, and many freshwater species have a very high livelihood value for local human communities. IUCN's freshwater focus is on the following taxonomic groups: fish; molluscs; crabs and crayfish; and dragonflies. Global assessment of these groups is being pursued through a series of regional projects, such as one for Africa that is currently being implemented.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe marine realm is poorly covered in the IUCN Red List, comprising less than 5 percent of the species included. IUCN has identified priority taxonomic groups of marine fish, invertebrates, plants (mangroves and seagrasses) and macro-algae (seaweeds). If these priority groups can be assessed, the number of marine species on the IUCN Red List will be increased more than six-fold.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future.\n\n### Limitations and exceptions:\nReporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List.", "origins": [ { "producer": "FishBase (Froese and Pauly, 2008), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FishBase database, FishBase, uri: https://www.fishbase.org/, date published: 2008. Indicator EN.FSH.THRD.NO (https://data.worldbank.org/indicator/EN.FSH.THRD.NO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.FSH.THRD.NO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_all_lu_mt_ce_ar5": { "title": "Total greenhouse gas emissions including LULUCF (Mt CO2e)", "description_from_producer": "A measure of annual emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphur hexafluoride (SF6)) from the energy, industry, waste, agriculture, and land use, land use changes, and forestry (LULUCF) sectors, standardized to carbon dioxide equivalent values. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and European Forest Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, , uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nEuropean Forest Observatory, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: European Commission (EC), date published: 2024. Indicator EN.GHG.ALL.LU.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.ALL.LU.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.ALL.LU.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_all_mt_ce_ar5": { "title": "Total greenhouse gas emissions excluding LULUCF (Mt CO2e)", "description_from_producer": "A measure of annual emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)) from the energy, industry, waste, and agriculture sectors, standardized to carbon dioxide equivalent values. This measure excludes GHG fluxes caused by Land Use Land Use Change and Forestry (LULUCF), as these fluxes have larger uncertainties. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.ALL.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.ALL.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.ALL.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_all_pc_ce_ar5": { "title": "Total greenhouse gas emissions excluding LULUCF per capita (t CO2e/capita)", "description_from_producer": "Total annual emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)) from the energy, industry, waste, and agriculture sectors, standardized to carbon dioxide equivalent values divided by the economy's population. This measure excludes GHG fluxes caused by Land Use Change and Forestry (LULUCF), as these fluxes have larger uncertainties.\n\n### Aggregation method:\nWeighted average using population as weights\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n?? =??\u00d7????\n\nWhere:\n\nE = Total emissions (kg, tons, or CO2-equivalent)\nA = Activity data (e.g., fuel consumption, production levels)\nEF = Emission factor (kg of pollutant per unit of activity)\n\nDepending on the pollutant, different tiers of complexity are used:\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\nTier 2 \u2013 Country/region-specific emission factors\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.ALL.PC.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.ALL.PC.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.ALL.PC.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_ch4_ag_mt_ce_ar5": { "title": "Methane (CH4) emissions from Agriculture (Mt CO2e)", "description_from_producer": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the agricultural sector. This includes emissions from livestock (IPCC 2006 codes 3.A.1 (enteric fermentation, 3.a.2 (manure management) and crops (IPCC 2006 codes 3.C.1 Emissions from biomass burning, 3.C.7 Rice cultivations). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CH4.AG.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CH4.AG.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CH4.AG.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_ch4_bu_mt_ce_ar5": { "title": "Methane (CH4) emissions from Building (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the building sector (subsector of the energy sector) including IPCC 2006 codes 1.A.4 Residential and other sectors, 1.A.5 Non-Specified. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CH4.BU.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CH4.BU.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CH4.BU.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_ch4_fe_mt_ce_ar5": { "title": "Methane (CH4) emissions from Fugitive Emissions (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from fugitive emissions (subsector of the energy sector) including IPCC 2006 codes 1.A.1.bc Petroleum Refining - Manufacture of Solid Fuels and Other Energy Industries, 1.B.1 Solid Fuels, 1.B.2 Oil and Natural Gas, 5.B. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CH4.FE.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CH4.FE.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CH4.FE.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_ch4_ic_mt_ce_ar5": { "title": "Methane (CH4) emissions from Industrial Combustion (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from industrial combustion (subsector of the energy sector) including IPCC 2006 code 1.A.2 Manufacturing Industries and Construction. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CH4.IC.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CH4.IC.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CH4.IC.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_ch4_ip_mt_ce_ar5": { "title": "Methane (CH4) emissions from Industrial Processes (Mt CO2e)", "description_from_producer": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from industrial processes including IPCC 2006 codes 2.A.1 Cement production, 2.A.2 Lime production, 2.A.3 Glass Production, 2.A.4 Other Process Uses of Carbonates, 2.B Chemical Industry, 2.C Metal Industry, 2.D Non-Energy Products from Fuels and Solvent Use, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use and 5.A Indirect N2O emissions from the atmospheric deposition of nitrogen in NOx and NH3. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CH4.IP.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CH4.IP.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CH4.IP.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_ch4_mt_ce_ar5": { "title": "Methane (CH4) emissions (total) excluding LULUCF (Mt CO2e)", "description_from_producer": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CH4.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CH4.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CH4.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_ch4_pi_mt_ce_ar5": { "title": "Methane (CH4) emissions from Power Industry (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from electricity and heat generation (subsector of the energy sector) including IPCC 2006 code 1.A.1.a. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CH4.PI.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CH4.PI.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CH4.PI.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_ch4_tr_mt_ce_ar5": { "title": "Methane (CH4) emissions from Transport (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the transportation sector (subsector of the energy sector) including IPCC 2006 codes 1.A.3.a Civil Aviation, 1.A.3.b_noRES Road Transportation no resuspension, 1.A.3.c Railways, 1.A.3.d Water-borne Navigation, 1.A.3.e Other Transportation. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CH4.TR.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CH4.TR.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CH4.TR.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_ch4_wa_mt_ce_ar5": { "title": "Methane (CH4) emissions from Waste (Mt CO2e)", "description_from_producer": "A measure of annual emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the waste sector. This includes emissions from solid waste (IPCC 2006 codes 4.A Solid Waste Disposal, 4.B Biological Treatment of Solid Waste, 4.C Incineration and Open Burning of Waste) and wastewater treatment (IPCC 2006 code 4.D Wastewater Treatment and Discharge). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CH4.WA.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CH4.WA.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CH4.WA.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_ch4_zg_ar5": { "title": "Methane (CH4) emissions (total) excluding LULUCF (% change from 1990)", "description_from_producer": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of methane (CH4), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5). Negative values indicate that the emission level for that year is lower than the emissions level in 1990.\n\n### Aggregation method:\nWeighted average using 1990 emission levels as weights\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CH4.ZG.AR5 (https://data.worldbank.org/indicator/EN.GHG.CH4.ZG.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CH4.ZG.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_ag_mt_ce_ar5": { "title": "Carbon dioxide (CO2) emissions from Agriculture (Mt CO2e)", "description_from_producer": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agricultural sector. This includes emissions from livestock (IPCC 2006 codes 3.A.1 (enteric fermentation, 3.a.2 (manure management) and crops (IPCC 2006 codes 3.C.1 Emissions from biomass burning, 3.C.2 Liming, 3.C.3 Urea application, 3.C.4 Direct N2O Emissions from managed soils, 3.C.5 Indirect N2O Emissions from managed soils, 3.C.6 Indirect N2O Emissions from manure management, 3.C.7 Rice cultivations). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.AG.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.AG.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.AG.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_bu_mt_ce_ar5": { "title": "Carbon dioxide (CO2) emissions from Building (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the building sector (subsector of the energy sector) including IPCC 2006 codes 1.A.4 Residential and other sectors, 1.A.5 Non-Specified. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.BU.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.BU.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.BU.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_fe_mt_ce_ar5": { "title": "Carbon dioxide (CO2) emissions from Fugitive Emissions (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from fugitive emissions (subsector of the energy sector) including IPCC 2006 codes 1.A.1.bc Petroleum Refining - Manufacture of Solid Fuels and Other Energy Industries, 1.B.1 Solid Fuels, 1.B.2 Oil and Natural Gas, 5.B. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.FE.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.FE.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.FE.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_ic_mt_ce_ar5": { "title": "Carbon dioxide (CO2) emissions from Industrial Combustion (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from industrial combustion (subsector of the energy sector) including IPCC 2006 code 1.A.2 Manufacturing Industries and Construction. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.IC.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.IC.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.IC.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_ip_mt_ce_ar5": { "title": "Carbon dioxide (CO2) emissions from Industrial Processes (Mt CO2e)", "description_from_producer": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from industrial processes including IPCC 2006 codes 2.A.1 Cement production, 2.A.2 Lime production, 2.A.3 Glass Production, 2.A.4 Other Process Uses of Carbonates, 2.B Chemical Industry, 2.C Metal Industry, 2.D Non-Energy Products from Fuels and Solvent Use, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use and 5.A Indirect N2O emissions from the atmospheric deposition of nitrogen in NOx and NH3. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.IP.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.IP.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.IP.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_lu_df_mt_ce_ar5": { "title": "Carbon dioxide (CO2) net fluxes from LULUCF - Deforestation (Mt CO2e)", "description_from_producer": "Net flux of carbon dioxide (CO2) in the category \"Deforestation\".\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.", "origins": [ { "producer": "Earth System Science Data (ESSD) - Carbon fluxes from land 2000\u20132020, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Carbon fluxes from land 2000\u20132020: bringing clarity on countries\u2019 reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD). Indicator EN.GHG.CO2.LU.DF.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.LU.DF.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.LU.DF.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_lu_fl_mt_ce_ar5": { "title": "Carbon dioxide (CO2) net fluxes from LULUCF - Forest Land (Mt CO2e)", "description_from_producer": "Net flux of carbon dioxide (CO2) in the category \"Forest land\".\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.", "origins": [ { "producer": "Earth System Science Data (ESSD) - Carbon fluxes from land 2000\u20132020, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Carbon fluxes from land 2000\u20132020: bringing clarity on countries\u2019 reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD). Indicator EN.GHG.CO2.LU.FL.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.LU.FL.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.LU.FL.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_lu_mt_ce_ar5": { "title": "Carbon dioxide (CO2) net fluxes from LULUCF - Total excluding non-tropical fires (Mt CO2e)", "description_from_producer": "Net flux of carbon dioxide (CO2) from Land Use, Land Use Change and Forestry LULUCF, excluding non-tropical fires at the country level.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.", "origins": [ { "producer": "Earth System Science Data (ESSD) - Carbon fluxes from land 2000\u20132020, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Carbon fluxes from land 2000\u20132020: bringing clarity on countries\u2019 reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD). Indicator EN.GHG.CO2.LU.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.LU.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.LU.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_lu_ol_mt_ce_ar5": { "title": "Carbon dioxide (CO2) net fluxes from LULUCF - Other Land (Mt CO2e)", "description_from_producer": "Net flux of carbon dioxide (CO2) in the category \"Other land\".\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.", "origins": [ { "producer": "Earth System Science Data (ESSD) - Carbon fluxes from land 2000\u20132020, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Carbon fluxes from land 2000\u20132020: bringing clarity on countries\u2019 reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD). Indicator EN.GHG.CO2.LU.OL.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.LU.OL.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.LU.OL.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_lu_os_mt_ce_ar5": { "title": "Carbon dioxide (CO2) net fluxes from LULUCF - Organic Soil (Mt CO2e)", "description_from_producer": "Net flux of carbon dioxide (CO2) in the category \"Organic soil\".\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.", "origins": [ { "producer": "Earth System Science Data (ESSD) - Carbon fluxes from land 2000\u20132020, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Carbon fluxes from land 2000\u20132020: bringing clarity on countries\u2019 reporting, uri: https://doi.org/10.5194/essd-14-4643-2022, note: Data available from https://doi.org/10.5281/zenodo.7190605, publisher: Earth System Science Data (ESSD). Indicator EN.GHG.CO2.LU.OS.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.LU.OS.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.LU.OS.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_mt_ce_ar5": { "title": "Carbon dioxide (CO2) emissions (total) excluding LULUCF (Mt CO2e)", "description_from_producer": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: International Energy Agency (IEA), date published: 2024. Indicator EN.GHG.CO2.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_pc_ce_ar5": { "title": "Carbon dioxide (CO2) emissions excluding LULUCF per capita (t CO2e/capita)", "description_from_producer": "Total annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF, standardized to carbon dioxide equivalent values divided by the economy's population. This measure excludes GHG fluxes caused by Land Use, Land-Use Change and Forestry (LULUCF), as these fluxes have larger uncertainties.\n\n### Aggregation method:\nWeighted average using population as weights\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.PC.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.PC.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.PC.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_pi_mt_ce_ar5": { "title": "Carbon dioxide (CO2) emissions from Power Industry (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from electricity and heat generation (subsector of the energy sector) including IPCC 2006 code 1.A.1.a. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.PI.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.PI.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.PI.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_rt_gdp_kd": { "title": "Carbon intensity of GDP (kg CO2e per constant 2015 US$ of GDP)", "description_from_producer": "Annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF divided by the GDP in constant 2021 US$.\n\n### Aggregation method:\nWeighted average using GDP as weights\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.RT.GDP.KD (https://data.worldbank.org/indicator/EN.GHG.CO2.RT.GDP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.RT.GDP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_rt_gdp_pp_kd": { "title": "Carbon intensity of GDP (kg CO2e per 2021 PPP $ of GDP)", "description_from_producer": "Annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF divided by the GDP in 2021 PPP $.\n\n### Aggregation method:\nWeighted average using GDP as weights\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\nWhere:\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.RT.GDP.PP.KD (https://data.worldbank.org/indicator/EN.GHG.CO2.RT.GDP.PP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.RT.GDP.PP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_tr_mt_ce_ar5": { "title": "Carbon dioxide (CO2) emissions from Transport (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the transportation sector (subsector of the energy sector) including IPCC 2006 codes 1.A.3.a Civil Aviation, 1.A.3.b_noRES Road Transportation no resuspension, 1.A.3.c Railways, 1.A.3.d Water-borne Navigation, 1.A.3.e Other Transportation. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.TR.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.TR.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.TR.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_wa_mt_ce_ar5": { "title": "Carbon dioxide (CO2) emissions from Waste (Mt CO2e)", "description_from_producer": "A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the waste sector. This includes emissions from solid waste (IPCC 2006 codes 4.A Solid Waste Disposal, 4.B Biological Treatment of Solid Waste, 4.C Incineration and Open Burning of Waste) and wastewater treatment (IPCC 2006 code 4.D Wastewater Treatment and Discharge). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.WA.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.WA.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.WA.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_co2_zg_ar5": { "title": "Carbon dioxide (CO2) emissions (total) excluding LULUCF (% change from 1990)", "description_from_producer": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5). Negative values indicate that the emission level for that year is lower than the emissions level in 1990.\n\n### Aggregation method:\nWeighted average using 1990 emission levels as weights\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.CO2.ZG.AR5 (https://data.worldbank.org/indicator/EN.GHG.CO2.ZG.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.CO2.ZG.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_fgas_ip_mt_ce_ar5": { "title": "F-gases emissions from Industrial Processes (Mt CO2e)", "description_from_producer": "A measure of annual emissions of fluorinated gases (hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphur hexafluoride (SF6)), from industrial processes including IPCC 2006 codes 2.B Chemical Industry, 2.C Metal Industry, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.FGAS.IP.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.FGAS.IP.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.FGAS.IP.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_n2o_ag_mt_ce_ar5": { "title": "Nitrous oxide (N2O) emissions from Agriculture (Mt CO2e)", "description_from_producer": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the agricultural sector. This includes emissions from livestock (IPCC 2006 codes 3.A.1 (enteric fermentation, 3.a.2 (manure management) and crops (IPCC 2006 codes 3.C.1 Emissions from biomass burning, 3.C.2 Liming, 3.C.3 Urea application, 3.C.4 Direct N2O Emissions from managed soils, 3.C.5 Indirect N2O Emissions from managed soils, 3.C.6 Indirect N2O Emissions from manure management, 3.C.7 Rice cultivations). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.N2O.AG.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.N2O.AG.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.N2O.AG.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_n2o_bu_mt_ce_ar5": { "title": "Nitrous oxide (N2O) emissions from Building (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the building sector (subsector of the energy sector) including IPCC 2006 codes 1.A.4 Residential and other sectors, 1.A.5 Non-Specified. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.N2O.BU.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.N2O.BU.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.N2O.BU.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_n2o_fe_mt_ce_ar5": { "title": "Nitrous oxide (N2O) emissions from Fugitive Emissions (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from fugitive emissions (subsector of the energy sector) including IPCC 2006 codes 1.A.1.bc Petroleum Refining - Manufacture of Solid Fuels and Other Energy Industries, 1.B.1 Solid Fuels, 1.B.2 Oil and Natural Gas, 5.B. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.N2O.FE.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.N2O.FE.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.N2O.FE.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_n2o_ic_mt_ce_ar5": { "title": "Nitrous oxide (N2O) emissions from Industrial Combustion (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from industrial combustion (subsector of the energy sector) including IPCC 2006 code 1.A.2 Manufacturing Industries and Construction. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.N2O.IC.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.N2O.IC.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.N2O.IC.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_n2o_ip_mt_ce_ar5": { "title": "Nitrous oxide (N2O) emissions from Industrial Processes (Mt CO2e)", "description_from_producer": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from industrial processes including IPCC 2006 codes 2.A.1 Cement production, 2.A.2 Lime production, 2.A.3 Glass Production, 2.A.4 Other Process Uses of Carbonates, 2.B Chemical Industry, 2.C Metal Industry, 2.D Non-Energy Products from Fuels and Solvent Use, 2.E Electronics Industry, 2.F Product Uses as Substitutes for Ozone Depleting Substances, 2.G Other Product Manufacture and Use and 5.A Indirect N2O emissions from the atmospheric deposition of nitrogen in NOx and NH3). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.N2O.IP.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.N2O.IP.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.N2O.IP.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_n2o_mt_ce_ar5": { "title": "Nitrous oxide (N2O) emissions (total) excluding LULUCF (Mt CO2e)", "description_from_producer": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.N2O.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.N2O.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.N2O.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_n2o_pi_mt_ce_ar5": { "title": "Nitrous oxide (N2O) emissions from Power Industry (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from electricity and heat generation (subsector of the energy sector) including IPCC 2006 code 1.A.1.a. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.N2O.PI.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.N2O.PI.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.N2O.PI.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_n2o_tr_mt_ce_ar5": { "title": "Nitrous oxide (N2O) emissions from Transport (Energy) (Mt CO2e)", "description_from_producer": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the transportation sector (subsector of the energy sector) including IPCC 2006 codes 1.A.3.a Civil Aviation, 1.A.3.b_noRES Road Transportation no resuspension, 1.A.3.c Railways, 1.A.3.d Water-borne Navigation, 1.A.3.e Other Transportation. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.N2O.TR.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.N2O.TR.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.N2O.TR.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_n2o_wa_mt_ce_ar5": { "title": "Nitrous oxide (N2O) emissions from Waste (Mt CO2e)", "description_from_producer": "A measure of annual emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the waste sector. This includes emissions from solid waste (IPCC 2006 codes 4.A Solid Waste Disposal, 4.B Biological Treatment of Solid Waste, 4.C Incineration and Open Burning of Waste) and wastewater treatment (IPCC 2006 code 4.D Wastewater Treatment and Discharge). The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.N2O.WA.MT.CE.AR5 (https://data.worldbank.org/indicator/EN.GHG.N2O.WA.MT.CE.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.N2O.WA.MT.CE.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_n2o_zg_ar5": { "title": "Nitrous oxide (N2O) emissions (total) excluding LULUCF (% change from 1990)", "description_from_producer": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of nitrous oxide (N2O), one of the six Kyoto greenhouse gases (GHG), from the agriculture, energy, waste, and industrial sectors, excluding LULUCF.. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5). Negative values indicate that the emission level for that year is lower than the emissions level in 1990.\n\n### Aggregation method:\nWeighted average using 1990 emission levels as weights\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\n\n\n\n\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n\n\n\n\n\n\n?? =??\u00d7????\n\n\n\n\n\n\nWhere:\n\n\n\n\n\n\nE = Total emissions (kg, tons, or CO2-equivalent)\n\n\n\n\n\n\nA = Activity data (e.g., fuel consumption, production levels)\n\n\n\n\n\n\nEF = Emission factor (kg of pollutant per unit of activity)\n\n\n\n\n\n\n\n\n\n\n\n\n\nDepending on the pollutant, different tiers of complexity are used:\n\n\n\n\n\n\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\n\n\n\n\n\n\nTier 2 \u2013 Country/region-specific emission factors\n\n\n\n\n\n\nTier 3 \u2013 Detailed process-based models (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nEDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, International Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.N2O.ZG.AR5 (https://data.worldbank.org/indicator/EN.GHG.N2O.ZG.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.N2O.ZG.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_ghg_tot_zg_ar5": { "title": "Total greenhouse gas emissions excluding LULUCF (% change from 1990)", "description_from_producer": "Change of emissions (as %) of current year with respect to emissions in baseline year 1990 emissions of the six greenhouse gases (GHG) covered by the Kyoto Protocol (carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphurhexafluoride (SF6)) from the energy, industry, waste, and agriculture sectors, standardized to carbon dioxide equivalent values. This measure excludes GHG fluxes caused by Land Use Change Land Use and Forestry (LULUCF), as these fluxes have larger uncertainties. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC's 5th Assessment Report (AR5) to combine different GHGs. Negative values indicate that the emission level for that year is lower than the emissions level in 1990.\n\n### Aggregation method:\nWeighted average using 1990 emission levels as weights\n\n### Statistical concept and methodology:\nMethodology: EDGAR compiles data from multiple authoritative sources, including: International Energy Agency (IEA) \u2013 Energy consumption data, United Nations Framework Convention on Climate Change (UNFCCC) \u2013 National GHG inventories, Food and Agriculture Organization (FAO) \u2013 Agricultural emissions, World Bank & National Statistics Offices \u2013 Socioeconomic and industrial data, Scientific Literature & IPCC Guidelines \u2013 Emission factors and methodologies.\n\nEDGAR follows the IPCC (Intergovernmental Panel on Climate Change) Guidelines for National Greenhouse Gas Inventories to estimate emissions. The core equation is:\n?? =??\u00d7????\nWhere:\nE = Total emissions (kg, tons, or CO2-equivalent)\nA = Activity data (e.g., fuel consumption, production levels)\nEF = Emission factor (kg of pollutant per unit of activity)\n\nDepending on the pollutant, different tiers of complexity are used:\nTier 1 \u2013 Default IPCC emission factors (simple estimation)\nTier 2 \u2013 Country/region-specific emission factors\nTier 3 \u2013 Uses detailed process-based modeling, facility-level data, or continuous emissions monitoring (highest accuracy)\n\n### Development relevance:\nAnthropogenic (human-caused) emissions of global greenhouse gases, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O) and F-gases, lead to an increase of the concentration of greenhouse gases in the atmosphere, which in turn causes atmospheric warming by trapping heat in the atmosphere (greenhouse gas effect). Atmospheric warming leads to climatic changes causing more frequent and extreme weather events and higher temperatures globally, leading to large impacts across the globe and particularly in developing countries that often have a limited means to adapt and build resilience. The international scientific community has warned that emissions need to decline to net zero by the middle of the 21st century to limit global warming to well below a 2deg C increase and help avoid the most consequential climate change impacts.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nClimate change is having a disproportionate impact on developing countries and if unabated will not only reverse past development progress and hinder poverty reduction but will also make future development more costly. Country level assessments of the potential climate change impacts on specific developing countries, performed as part of the World Bank\u2019s Country Climate and Development Reports (CCDRs), show that climate change will have a significant impact on developing countries\u2019 economies, ranging from about 0.5% of GDP for higher income developing countries to over 13% for the lowest income developing countries. The costs of partial adaptation to these changes will be significant as well -- ranging between 1 and 10% of developing countries\u2019 GDP.\n\n### Limitations and exceptions:\nGlobal GHG emissions are currently not directly measurable, but approaches for their estimation exist, and numerous sources exist to supply data for this indicator. Reputable scientific organizations produce these data for use for research, policy analysis, climate negotiations, and broader public communications. The estimated accuracy from fossil fuel combustion and industrial processes are quite high, as quantities of fossil fuels and other emissive materials (such as cement and steel) produced are well known. For these sectors, emissions estimates are roughly accurate to within 10% when aggregated to the global level and between 4% and 35% at the country level (Crippa et al., 2023). For non- combustion and non- industrial process emissions, the accuracy is lower. Agricultural emissions, for example, depend upon many factors including the type of crops grown and livestock raised, specific agricultural practices, and other climate and non-climate factors. For these emissions, the accuracy is lower\u2014around 30% for CH4 and fluorinated gases (HFCs, PFCs, and SF6).", "origins": [ { "producer": "Joint Research Centre (European Commission) and International Energy Agency (IEA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, Joint Research Centre (JRC) - European Commission, uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024;\nInternational Energy Agency (IEA), uri: https://edgar.jrc.ec.europa.eu/dataset_ghg2024, publisher: JRC European Commission, date published: 2024. Indicator EN.GHG.TOT.ZG.AR5 (https://data.worldbank.org/indicator/EN.GHG.TOT.ZG.AR5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.GHG.TOT.ZG.AR5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_hpt_thrd_no": { "title": "Plant species (higher), threatened", "description_from_producer": "A threatened species is likely to become endangered within the foreseeable future, throughout all or a significant portion of its range. Threatened species are species classified by the IUCN as Critically Endangered (CR), Endangered (EN) and Vulnerable (VU).\n\nHigher plants are native vascular plant species.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threatened species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\n\n### Development relevance:\nThe number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN guides conservation activities of governments, NGOs and scientific institutions. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe plants and animals assessed for the IUCN Red List are the bearers of genetic diversity and the building blocks of ecosystems, and information on their conservation status and distribution provides the foundation for making informed decisions about conserving biodiversity from local to global levels. Only a small number of the world's plant and animal species have been assessed. In addition to the many thousands of species which have not yet been assessed so far, other species not included on the IUCN Red List are those that went extinct before 1500 AD and the \"Least Concern\" (plants that have been evaluated to have a low risk of extinction) species that have not yet been data based.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future.\n\n### Limitations and exceptions:\nReporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAlthough there are over 12,000 plant species on the IUCN Red List, fewer than one thousand of these are properly documented. To help address this gap, IUCN is pursuing global assessments of plant species of value to people including species of high economic value. The conifer and cycad species already on the IUCN Red List need to be fully documented. IUCN is also developing a tool to assist with preliminary assessments of plant species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List.", "origins": [ { "producer": "IUCN Red List (IUCN and UNEP-WCMC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The IUCN Red List of Threatened Species, UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.iucnredlist.org/;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.iucnredlist.org/. Indicator EN.HPT.THRD.NO (https://data.worldbank.org/indicator/EN.HPT.THRD.NO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.HPT.THRD.NO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "higher", "short_unit": "higher", "display": { "shortUnit": "higher", "unit": "higher" } }, "en_mam_thrd_no": { "title": "Mammal species, threatened", "description_from_producer": "A threatened species is likely to become endangered within the foreseeable future, throughout all or a significant portion of its range. Threatened species are species classified by the IUCN as Critically Endangered (CR), Endangered (EN) and Vulnerable (VU).\n\nMammal species are mammals excluding whales and porpoises.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Species assessed as Critically Endangered (CR), Endangered (EN) or Vulnerable (VU) are referred to as \"threatened\" species. The International Union for Conservation of Nature (IUCN) Red List of Threatened Species collects and disseminates information on the global threated species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProportion of threatened species is only reported for the more completely evaluated groups (i.e., >90% of species evaluated). Also, the reported percentage of threatened species for each group is presented as a best estimate within a range of possible values bounded by lower and upper estimates:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLower estimate = % threatened extant species if all Data Deficient species are not threatened, i.e., (CR + EN + VU) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBest estimate = % threatened extant species if Data Deficient species are equally threatened as data sufficient species, i.e., (CR + EN + VU) / (total assessed - EX - DD)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUpper estimate = % threatened extant species if all Data Deficient species are threatened, i.e., (CR + EN + VU + DD) / (total assessed - EX)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAdditional information on ecology and habitat preferences, threats, and conservation action are also collated and assessed as part of Red List process.\n\n### Development relevance:\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. The number of threatened species is an important measure of the immediate need for conservation in an area. Global analyses of the status of threatened species have been carried out for few groups of organisms. Only for mammals, birds, and amphibians has the status of virtually all known species been assessed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThreatened species are defined using the International Union for Conservation of Nature's (IUCN) classification: endangered (in danger of extinction and unlikely to survive if causal factors continue operating) and vulnerable (likely to move into the endangered category in the near future if causal factors continue operating).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe International Union for Conservation of Nature (IUCN) Red List of Threatened Species is widely recognized as the most comprehensive, objective global approach for evaluating the conservation status of plant and animal species. The IUCN draws on and mobilizes a network of scientists and partner organizations working in almost every country in the world, who collectively hold what is likely the most complete scientific knowledge base on the biology and conservation status of species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nthe IUCN Red List covers a comprehensive assessment of the conservation status of the world's 5,488 mammal species, including global summary statistics, individual species accounts/threat category, range map, ecology information, and some other data. Mammal species are found spread across the globe, with the exception of the land mass of Antarctica. Nearly one-quarter of the world's mammal species are known to be globally threatened or extinct, 63 percent are known to not be threatened, and 15 percent have insufficient data to determine their threat status. Habitat loss, affecting over 2,000 mammal species, is the greatest threat globally. The second greatest threat is utilization which is affecting over 900 mammal species, mainly those in Asia.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDirect threats to species are the proximate human activities or processes that have impacted, are impacting, or may impact the status of the taxon being assessed (e.g., unsustainable fishing or logging). Direct threats are synonymous with sources of stress and proximate pressures. Threats can be past (historical, unlikely to return or historical, likely to return), ongoing, and/or likely to occur in the future.\n\n### Limitations and exceptions:\nReporting the proportion of threatened species on the Red List is complicated by the fact that not all species groups have been fully evaluated, and also by the fact that some species have so little information available that they can only be assessed as Data Deficient (DD). For many of the incompletely evaluated groups, assessment efforts have focused on species that are likely to be threatened; therefore any percentage of threatened species reported for these groups would be heavily biased (i.e., the percentage of threatened species would likely be an overestimate).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSome parts of the world, such as the Andes, Central and West Africa, Angola, parts of South and Southeast Asia, and Melanesia, still have sparse information available of their mammal faunas. In addition, many species' names, especially in the tropics, actually represent complexes of several species that have not yet been resolved. The information on the relative importance of different threatening processes to mammal species is incomplete. IUCN codes all threats that appear to have an important impact, but not their relative importance for each species.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSince IUCN has evaluated extinction risk for less than 5 percent of the world's described species, IUCN cannot provide an overall estimate for how many of the planet's species are threatened. For those groups that have been comprehensively evaluated, the proportion of threatened species can be calculated, but the number of threatened species is often uncertain because it is not known whether Data Deficient species are actually threatened or not.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas. Also, because of differences in definitions, reporting practices, and reporting periods, cross-country comparability of threatened species is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn order to ensure global uniformity when describing the habitat in which a taxon (a taxonomic group of any rank) occurs, the threats to a taxon, what conservation actions are in place or are needed, and whether or not the taxon is utilized, a set of standard terms, called Classification Schemes, are being developed, for documenting taxonomy on the IUCN Red List.", "origins": [ { "producer": "IUCN Red List (IUCN and UNEP-WCMC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The IUCN Red List of Threatened Species, UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.iucnredlist.org/;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.iucnredlist.org/. Indicator EN.MAM.THRD.NO (https://data.worldbank.org/indicator/EN.MAM.THRD.NO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.MAM.THRD.NO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_pop_dnst": { "title": "Population density (people per sq. km of land area)", "description_from_producer": "Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population density is midyear population divided by land area in square kilometers. This ratio can be calculated for any territorial unit for any point in time, depending on the source of the population data. Population estimates are prepared by World Bank staff from variety of sources. They are based on the de facto definition of population and include all residents regardless of legal status or citizenship, within the physical boundaries of a country and under the jurisdiction of that country's political control. Refugees not permanently settled in the country of asylum are considered part of the population of their country of origin. Population numbers are either current census data or historical census data extrapolated through demographic methods. The count also excludes visitors from overseas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation density is calculated by dividing midyear population by land area in a country. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship - except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.\n\n### Development relevance:\nPopulation estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries; in developing countries errors may be substantial because of limits in the transport, communications, and other resources required conducting and analyzing a full census.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPopulation density is a measure of the intensity of land-use, and can be calculated for a block, city, county, state, country, continent or the entire world. Considering that over half of the Earth's land mass consists of areas inhospitable to human inhabitation, such as deserts and high mountains, and that population tends to cluster around seaports and fresh water sources, a simple number of population density by itself does not give any meaningful measurement of human population density.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSeveral of the most densely populated territories in the world are city-states, microstates, or dependencies.[6][7] These territories share a relatively small area and a high urbanization level, with an economically specialized city population drawing also on rural resources outside the area, illustrating the difference between high population density and overpopulation.\n\n### Limitations and exceptions:\nCurrent population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data are provided by the United Nations Population Division and other agencies. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "FAO population estimates, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO);\nWorld Bank population estimates, World Bank (WB), publisher: World Bank (WB). Indicator EN.POP.DNST (https://data.worldbank.org/indicator/EN.POP.DNST). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.POP.DNST", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "people per sq. km of land area", "short_unit": "people", "display": { "shortUnit": "people", "unit": "people per sq. km of land area" } }, "en_pop_el5m_ru_zs": { "title": "Rural population living in areas where elevation is below 5 meters (% of total population)", "description_from_producer": "Rural population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population counts in low elevation zones in the year 1990 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2000 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2010 derived from the application of United Nations 2000-2010 national growth rates to year 2000 population data from GRUMPv1 ( see documentation for full description of methodologies ).\nStatistical concept(s):", "origins": [ { "producer": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, NASA Earthdata GIS, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112, date published: 202112;\nCUNY Institute for Demographic Research (CIDR) - City University of New York, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112. Indicator EN.POP.EL5M.RU.ZS (https://data.worldbank.org/indicator/EN.POP.EL5M.RU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.POP.EL5M.RU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total population" } }, "en_pop_el5m_ur_zs": { "title": "Urban population living in areas where elevation is below 5 meters (% of total population)", "description_from_producer": "Urban population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population counts in low elevation zones in the year 1990 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2000 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2010 derived from the application of United Nations 2000-2010 national growth rates to year 2000 population data from GRUMPv1 ( see documentation for full description of methodologies ).\nStatistical concept(s):", "origins": [ { "producer": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, NASA Earthdata GIS, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112, date published: 202112;\nCUNY Institute for Demographic Research (CIDR) - City University of New York, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112. Indicator EN.POP.EL5M.UR.ZS (https://data.worldbank.org/indicator/EN.POP.EL5M.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.POP.EL5M.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total population" } }, "en_pop_el5m_zs": { "title": "Population living in areas where elevation is below 5 meters (% of total population)", "description_from_producer": "Population below 5m is the percentage of the total population living in areas where the elevation is 5 meters or less.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population counts in low elevation zones in the year 1990 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2000 as described by GRUMPv1 input estimates allocated into 3 arc second grid cells. Population counts in low elevation zones in the year 2010 derived from the application of United Nations 2000-2010 national growth rates to year 2000 population data from GRUMPv1 (see documentation for full description of methodologies).\nStatistical concept(s):\n\n### Development relevance:\nScientists use the terms climate change and global warming to refer to the gradual increase in the Earth's surface temperature that has accelerated since the industrial revolution and especially over the past two decades. Most global warming has been caused by human activities that have changed the chemical composition of the atmosphere through a buildup of greenhouse gases - primarily carbon dioxide, methane, and nitrous oxide. Rising global temperatures will cause sea level rise and alter local climate conditions, affecting forests, crop yields, and water supplies, and may affect human health, animals, and many types of ecosystems.\n\n### Limitations and exceptions:\nThe 2007 Intergovernmental Panel on Climate Change's (IPCC) assessment report concluded that global warming is \u201cunequivocal\u201d and gave the strongest warning yet about the role of human activities. The report estimated that sea levels would rise approximately 49 centimeters over the next 100 years, with a range of uncertainty of 20\u201386 centimeters. That will lead to increased coastal flooding through direct inundation and a higher base for storm surges, allowing flooding of larger areas and higher elevations. Climate model simulations predict an increase in average surface air temperature of about 2.5\u00b0C by 2100 (Kattenberg and others 1996) and increase of \u201ckiller\u201d heat waves during the warm season (Karl and others 1997).", "origins": [ { "producer": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, NASA Earthdata GIS, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Low Elevation Coastal Zone (LECZ) Urban-Rural Population and Land Area Estimates, Version 3, Center For International Earth Science Information Network (CIESIN) - Columbia University, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112, date published: 202112;\nCUNY Institute for Demographic Research (CIDR) - City University of New York, uri: https://doi.org/10.7927/d1x1-d702, publisher: NASA Earthdata GIS, date accessed: 202112. Indicator EN.POP.EL5M.ZS (https://data.worldbank.org/indicator/EN.POP.EL5M.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.POP.EL5M.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total population" } }, "en_pop_slum_ur_zs": { "title": "Population living in slums (% of urban population)", "description_from_producer": "Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, housing durability, and security of tenure, as adopted in the Millennium Development Goal Target 7.D. The successor, the Sustainable Development Goal 11.1.1, considers inadequate housing (housing affordability) to complement the above definition of slums/informal settlements.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, housing durability, and security of tenure, as adopted in the Millennium Development Goal Target 7.D. The successor, the Sustainable Development Goal 11.1.1, considers inadequate housing (housing affordability) to complement the above definition of slums/informal settlements.", "origins": [ { "producer": "UN Human Settlements Programme (UN-Habitat), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Urban Indicators Database, UN Human Settlements Programme (UN-Habitat), uri: https://data.unhabitat.org/. Indicator EN.POP.SLUM.UR.ZS (https://data.worldbank.org/indicator/EN.POP.SLUM.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.POP.SLUM.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of urban population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of urban population" } }, "en_urb_lcty": { "title": "Population in largest city", "description_from_producer": "Population in largest city is the urban population living in the country's largest metropolitan area.\n\n### Statistical concept and methodology:\nMethodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)\n\n### Development relevance:\nA metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing countries will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation.\n\n### Limitations and exceptions:\nAggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population.", "origins": [ { "producer": "UN Population Division (World Urbanization Prospects 2018), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018. Indicator EN.URB.LCTY (https://data.worldbank.org/indicator/EN.URB.LCTY). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.URB.LCTY", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_urb_lcty_ur_zs": { "title": "Population in the largest city (% of urban population)", "description_from_producer": "Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)\n\n### Development relevance:\nA metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing countries will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation.\n\n### Limitations and exceptions:\nAggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population.", "origins": [ { "producer": "UN Population Division (World Urbanization Prospects 2018), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018. Indicator EN.URB.LCTY.UR.ZS (https://data.worldbank.org/indicator/EN.URB.LCTY.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.URB.LCTY.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of urban population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of urban population" } }, "en_urb_mcty": { "title": "Population in urban agglomerations of more than 1 million", "description_from_producer": "Population in urban agglomerations of more than one million is the country's population living in metropolitan areas that in 2018 had a population of more than one million people.\n\n### Statistical concept and methodology:\nMethodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)\n\n### Development relevance:\nAccording to the United Nations, an Urban Agglomeration refers to the de facto population contained within the contours of a contiguous territory inhabited at urban density levels without regard to administrative boundaries. It usually incorporates the population in a city or town plus that in the sub-urban areas lying outside of but being adjacent to the city boundaries. In general, an urban agglomeration is an extended city or town area comprising the built-up area of a central place and any suburbs linked by continuous urban area. INSEE, the French Statistical Institute, uses the term unit\u00e9 urbaine, which means continuous urbanized area. There are differences in definitions of what does and does not constitute an \"agglomeration\", as well as differences in statistical and geographical methodology. Some of the well-known urban agglomerations of the world are Tokyo, New York City, Mexico City, New Delhi, and Seoul.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing countries will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation.\n\n### Limitations and exceptions:\nDue to varying definitions, it is not possible to compare different agglomerations around the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population.", "origins": [ { "producer": "UN Population Division (World Urbanization Prospects 2018), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018. Indicator EN.URB.MCTY (https://data.worldbank.org/indicator/EN.URB.MCTY). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.URB.MCTY", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "en_urb_mcty_tl_zs": { "title": "Population in urban agglomerations of more than 1 million (% of total population)", "description_from_producer": "Population in urban agglomerations of more than one million is the percentage of a country's population living in metropolitan areas that in 2018 had a population of more than one million people.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Urban population refers to people living in urban areas as defined by national statistical offices. The indicator is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. The United Nations Population Division and other agencies provide current population estimates for developing countries that lack recent census data and pre- and post-census estimates for countries with census data. The cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in the model and in the data. Because the five-year age group is the cohort unit and five-year period data are used, interpolations to obtain annual data or single age structure may not reflect actual events or age composition.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\" Typically, a community or settlement with a population of 2,000 or more is considered urban, but national definitions are most commonly based on size of locality. Eurostat defines urban areas as clusters of contiguous grid cells of 1 km2 with a density of at least 300 inhabitants per km2 and a minimum population of 5,000. Further it defines high-density cluster as contiguous grid cells of 1 km2 with a density of at least 1,500 inhabitants per km2 and a minimum population of 50,000.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe population of a city or metropolitan area depends on the boundaries chosen. For example, in 1990 Beijing, China, contained 2.3 million people in 87 square kilometers of \"inner city\" and 5.4 million in 158 square kilometers of \"core city.\" The population of \"inner city and inner suburban districts\" was 6.3 million and that of \"inner city, inner and outer suburban districts, and inner and outer counties\" was 10.8 million. (Most countries use the last definition.)\n\n### Development relevance:\nAccording to the United Nations, an Urban Agglomeration refers to the de facto population contained within the contours of a contiguous territory inhabited at urban density levels without regard to administrative boundaries. It usually incorporates the population in a city or town plus that in the sub-urban areas lying outside of but being adjacent to the city boundaries. In general, an urban agglomeration is an extended city or town area comprising the built-up area of a central place and any suburbs linked by continuous urban area. INSEE, the French Statistical Institute, uses the term unit\u00e9 urbaine, which means continuous urbanized area. There are differences in definitions of what does and does not constitute an \"agglomeration\", as well as differenced in statistical and geographical methodology. Some of the well-known urban agglomerations of the world are Tokyo, New York City, Mexico City, New Delhi, and Seoul.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nA metropolitan area includes the urban area, and its satellite cities plus intervening rural land that is socio-economically connected to the urban core city, typically by employment ties through commuting, with the urban core city being the primary labor market. According to the United Nations' definition, a metropolitan area includes both the contiguous territory inhabited at urban levels of residential density and additional surrounding areas of lower settlement density that are also under the direct influence of the city (e.g., through frequent transport, road linkages, commuting facilities etc.).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service. For the first time ever, the majority of the world's population lives in a city, and this proportion continues to grow. One hundred years ago, 2 out of every 10 people lived in an urban area. By 1990, less than 40 percent of the global population lived in a city, but as of early 2010s, more than half of all people live in an urban area. By 2030, 6 out of every 10 people will live in a city, and by 2050, this proportion will increase to 7 out of 10 people. About half of all urban dwellers live in cities with between 100,000-500,000 people, and fewer than 10% of urban dwellers live in megacities (a city with a population of more than 10 million, as defined by UN HABITAT). Currently, the number of urban residents is growing by nearly 60 million every year.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBy the middle of the 21st century, the urban population will almost double, reaching 6.4 billion in 2050. Almost all urban population growth in the next 30 years will occur in cities of developing countries. By the middle of the 21st century, it is estimated that the urban population of developing countries will more than double, reaching almost 5.2 billion in 2050. In high-income countries, the urban population is expected to remain largely unchanged over the next two decades, reaching to just over 1 billion by 2025. In these countries, immigration (legal and illegal) will account for more than two-thirds of urban growth. Without immigration, the urban population in these countries would most likely decline or remain static.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment. Poverty is growing faster in urban than in rural areas. According to UN one billion people live in urban slums, which are typically overcrowded, polluted and dangerous, and lack basic services such as clean water and sanitation.\n\n### Limitations and exceptions:\nDue to varying definitions, it is not possible to compare different agglomerations around the world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAggregation of urban and rural population may not add up to total population because of different country coverage. There is no consistent and universally accepted standard for distinguishing urban from rural areas, in part because of the wide variety of situations across countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries. For example, in Botswana, agglomeration of 5,000 or more inhabitants where 75 per cent of the economic activity is non-agricultural is considered \"urban\" while in Iceland localities of 200 or more inhabitants, and in Peru population centers with 100 or more dwellings, are considered \"urban.\" In the United States places of 2,500 or more inhabitants, generally having population densities of 1,000 persons per square mile or more are considered \"urban\".\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers. According to China's State Statistical Bureau, by the end of 1996 urban residents accounted for about 43 percent of China's population, more than double the 20 percent considered urban in 1994. In addition to the continuous migration of people from rural to urban areas, one of the main reasons for this shift was the rapid growth in the hundreds of towns reclassified as cities in recent years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population.", "origins": [ { "producer": "UN Population Division (World Urbanization Prospects 2018), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Urbanization Prospects 2018, United Nations (UN), uri: https://population.un.org/wup/, publisher: UN Population Division, date published: 2018. Indicator EN.URB.MCTY.TL.ZS (https://data.worldbank.org/indicator/EN.URB.MCTY.TL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EN.URB.MCTY.TL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total population" } }, "er_fsh_aqua_mt": { "title": "Aquaculture production (metric tons)", "description_from_producer": "Aquaculture is understood to mean the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants. Aquaculture production specifically refers to output from aquaculture activities, which are designated for final harvest for consumption.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Aquaculture production specifically refers to output from aquaculture activities, which are designated for final harvest for consumption. At this time, harvest for ornamental purposes is not included.\n\n### Development relevance:\nAquaculture is understood to mean the farming of aquatic organisms including fish, molluscs, crustaceans and aquatic plants. Farming implies some form of intervention in the rearing process to enhance production, such as regular stocking, feeding, protection from predators, etc. Farming also implies individual or corporate ownership of the stock being cultivated. For statistical purposes, aquatic organisms which are harvested by an individual of corporate body which has owned them throughout their rearing period contribute to aquaculture while aquatic organisms which are exploitable by public as a common property resource, with or without appropriate licences, are the harvest of fisheries.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Food and Agriculture Organization., Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator ER.FSH.AQUA.MT (https://data.worldbank.org/indicator/ER.FSH.AQUA.MT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.FSH.AQUA.MT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "metric tons", "display": { "unit": "metric tons" } }, "er_fsh_capt_mt": { "title": "Capture fisheries production (metric tons)", "description_from_producer": "Capture fisheries production measures the volume of fish catches landed by a country for all commercial, industrial, recreational and subsistence purposes.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Information on capture production is collected annually from relevant national offices concerned with fishery statistics, by means of a system of standardized forms, which list for each country the relative species items and fishing areas breakdown.\n\n\n\n\n\n\n\n\nIn the case of some \"aquatic products\", data are also obtained from trade associations or other specialized international organizations to which data are also submitted. In this way the statistics are reviewed by subject matter specialists.\n\n\n\n\n\n\n\n\nData concerning the nominal catch of certain major groups are generally reviewed in collaboration with the regional agency concerned. For example, for ISSCAAP group 36 (Tunas, bonitos and billfishes) data provided by the national correspondents are often replaced by the \"best scientific estimates\" produced by regional bodies collecting tuna catch statistics (i.e. ICCAT, IOTC, SPC and IATTC.)\n\n\n\n\n\n\n\n\nSee https://www.fao.org/fishery/en/collection/capture", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Food and Agriculture Organization., Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator ER.FSH.CAPT.MT (https://data.worldbank.org/indicator/ER.FSH.CAPT.MT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.FSH.CAPT.MT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "metric tons", "display": { "unit": "metric tons" } }, "er_fsh_prod_mt": { "title": "Total fisheries production (metric tons)", "description_from_producer": "Total fisheries production measures the volume of aquatic species caught by a country for all commercial, industrial, recreational and subsistence purposes. The harvest from mariculture, aquaculture and other kinds of fish farming is also included.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Information on capture production is collected annually from relevant national offices concerned with fishery statistics, by means of a system of standardized forms, which list for each country the relative species items and fishing areas breakdown.\n\n\n\n\n\n\n\n\nIn the case of some \"aquatic products\", data are also obtained from trade associations or other specialized international organizations to which data are also submitted. In this way the statistics are reviewed by subject matter specialists.\n\n\n\n\n\n\n\n\nData concerning the nominal catch of certain major groups are generally reviewed in collaboration with the regional agency concerned. For example, for ISSCAAP group 36 (Tunas, bonitos and billfishes) data provided by the national correspondents are often replaced by the \"best scientific estimates\" produced by regional bodies collecting tuna catch statistics (i.e. ICCAT, IOTC, SPC and IATTC.)\n\n\n\n\n\n\n\n\nSee https://www.fao.org/fishery/en/collection/capture", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Food and Agriculture Organization., Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO). Indicator ER.FSH.PROD.MT (https://data.worldbank.org/indicator/ER.FSH.PROD.MT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.FSH.PROD.MT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "metric tons", "display": { "unit": "metric tons" } }, "er_gdp_fwtl_m3_kd": { "title": "Water productivity, total (constant 2015 US$ GDP per cubic meter of total freshwater withdrawal)", "description_from_producer": "Water productivity is calculated as GDP in constant prices divided by annual total water withdrawal.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Water productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. GDP data are from World Bank's national accounts files.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants).\n\n### Development relevance:\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectoral planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n### Limitations and exceptions:\nA common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.", "origins": [ { "producer": "Food and Agriculture Organization (AQUASTAT), World Bank, and OECD", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), publisher: Food and Agriculture Organization of the United Nations (FAO);\nWorld Bank GDP estimates, World Bank (WB), publisher: World Bank (WB);\nOECD GDP estimates, Organisation for Economic Co-operation and Development (OECD), publisher: Organisation for Economic Co-operation and Development (OECD). Indicator ER.GDP.FWTL.M3.KD (https://data.worldbank.org/indicator/ER.GDP.FWTL.M3.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.GDP.FWTL.M3.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$ GDP per cubic meter of total freshwater withdrawal", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$ GDP per cubic meter of total freshwater withdrawal" } }, "er_h2o_fwag_zs": { "title": "Annual freshwater withdrawals, agriculture (% of total freshwater withdrawal)", "description_from_producer": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture are total withdrawals for irrigation and livestock production. Data are for the most recent year available for 1987-2002.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator measures the pressure on the renewable water resources of a country caused by irrigation. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants).\nStatistical concept(s):\n\n### Development relevance:\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectoral planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n### Limitations and exceptions:\nA common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems.", "origins": [ { "producer": "AQUASTAT - FAO's Global Information System on Water and Agriculture, FAO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529. Indicator ER.H2O.FWAG.ZS (https://data.worldbank.org/indicator/ER.H2O.FWAG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.H2O.FWAG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total freshwater withdrawal", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total freshwater withdrawal" } }, "er_h2o_fwdm_zs": { "title": "Annual freshwater withdrawals, domestic (% of total freshwater withdrawal)", "description_from_producer": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Domestic water withdrawal, sometimes used interchangeably with municipal water withdrawal, focuses on human needs (drinking, cooking, cleaning, and sanitation). Data includes renewable freshwater resources, potential over-abstraction of renewable groundwater, withdrawal of fossil groundwater, and the potential use of desalinated water or treated wastewater. It is usually computed as the total water withdrawn by the public distribution network, and includes that part of the industries, which is connected to the municipal network. The ratio between the net consumption and the water withdrawn can vary from 5 to 15 percent in urban areas and from 10 to 50 percent in rural areas.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\nStatistical concept(s):\n\n### Development relevance:\nUNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations.\n\n### Limitations and exceptions:\nA common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems.", "origins": [ { "producer": "AQUASTAT - FAO's Global Information System on Water and Agriculture, FAO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529. Indicator ER.H2O.FWDM.ZS (https://data.worldbank.org/indicator/ER.H2O.FWDM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.H2O.FWDM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total freshwater withdrawal", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total freshwater withdrawal" } }, "er_h2o_fwin_zs": { "title": "Annual freshwater withdrawals, industry (% of total freshwater withdrawal)", "description_from_producer": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for industry are total withdrawals for direct industrial use (including withdrawals for cooling thermoelectric plants). Data are for the most recent year available for 1987-2002.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Annual industrial freshwater withdrawals include renewable water resources as well as potential over-abstraction of renewable groundwater or potential use of desalinated water or treated wastewater. It includes water for the cooling of thermoelectric plants, but it does not include hydropower.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for industry are total withdrawals for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\n\n### Development relevance:\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture. UNESCO estimates that Industrial uses account for about 20 percent of global freshwater withdrawals. Of this, 57-69 percent is used for hydropower and nuclear power generation, 30-40 percent for industrial processes, and 0.5-3 percent for thermal power generation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations.\n\n### Limitations and exceptions:\nA common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.", "origins": [ { "producer": "AQUASTAT - FAO's Global Information System on Water and Agriculture, FAO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529. Indicator ER.H2O.FWIN.ZS (https://data.worldbank.org/indicator/ER.H2O.FWIN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.H2O.FWIN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total freshwater withdrawal", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total freshwater withdrawal" } }, "er_h2o_fwst_zs": { "title": "Level of water stress: freshwater withdrawal as a proportion of available freshwater resources", "description_from_producer": "The level of water stress: freshwater withdrawal as a proportion of available freshwater resources is the ratio between total freshwater withdrawn by all major sectors and total renewable freshwater resources, after taking into account environmental water requirements. Main sectors, as defined by ISIC standards, include agriculture; forestry and fishing; manufacturing; electricity industry; and services. This indicator is also known as water withdrawal intensity.\n\n### Statistical concept and methodology:\nMethodology: Proportion of total renewable water resources withdrawn is the total volume of groundwater and surface water withdrawn from their sources for human use (in the agricultural, municipal and industrial sectors), expressed as a percentage of the total actual renewable water resources. The terms water resources and water withdrawal are understood as freshwater resources and freshwater withdrawal. Water withdrawal is estimated for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries, at country level and expressed in km3/year. The total actual renewable water resources for a country or region are defined as the sum of internal renewable water resources and the external renewable water resources, also expressed in km3/year. The indicator is computed by dividing total water withdrawal by total actual renewable water resources minus environmental requirements and expressed in percentage points.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal freshwater withdrawal is the volume of freshwater extracted from its source (rivers, lakes, aquifers) for agriculture, industries and municipalities. It is estimated at the country level for the following three main sectors: agriculture, municipalities (including domestic water withdrawal) and industries. Freshwater withdrawal includes primary freshwater (not withdrawn before), secondary freshwater (previously withdrawn and returned to rivers and groundwater, such as discharged wastewater and agricultural drainage water) and fossil groundwater. It does not include non-conventional water, i.e. direct use of treated wastewater, direct use of agricultural drainage water and desalinated water. Total freshwater withdrawal is in general calculated as being the sum of total water withdrawal by sector minus direct use of wastewater, direct use of agricultural drainage water and use of desalinated water.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal renewable freshwater resources are expressed as the sum of internal and external renewable water resources. The terms \u201cwater resources\u201d and \u201cwater withdrawal\u201d are understood here as freshwater resources and freshwater withdrawal. Internal renewable water resources are defined as the long-term average annual flow of rivers and recharge of groundwater for a given country generated from endogenous precipitation. External renewable water resources refer to the flows of water entering the country, taking into consideration the quantity of flows reserved to upstream and downstream countries through agreements or treaties.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnvironmental water requirements (Env.) are the quantities of water required to sustain freshwater and estuarine ecosystems. Water quality and also the resulting ecosystem services are excluded from this formulation which is confined to water volumes. This does not imply that quality and the support to societies which are dependent on environmental flows are not important and should not be taken care of. Methods of computation of Env. are extremely variable and range from global estimates to comprehensive assessments for river reaches. Water volumes can be expressed in the same units as the total freshwater withdrawal, and then as percentages of the available water resources.\n\n### Development relevance:\nThe level of water stress can show the degree to which water resources are being exploited to meet the country's water demand. It measures a country's pressure on its water resources and therefore the challenge on the sustainability of its water use. It tracks progress in regard to \u201cwithdrawals and supply of freshwater to address water scarcity\u201d, i.e. the environmental component of target 6.4. It also shows to what extent water resources are already used, and signals the importance of effective supply and demand management policies. It indicates the likelihood of increasing competition and conflict between different water uses and users in a situation of increasing water scarcity. Increased water stress, shown by an increase in the value of the indicator, has potentially negative effects on the sustainability of the natural resources and on economic development. On the other hand, low values of water stress indicate that water does not represent a particular challenge for economic development and sustainability.\n\n### Limitations and exceptions:\nWater withdrawal as a percentage of water resources is a good indicator of pressure on limited water resources, one of the most important natural resources. However, it only partially addresses the issues related to sustainable water management. Supplementary indicators that capture the multiple dimensions of water management would combine data on water demand management, behavioural changes with regard to water use and the availability of appropriate infrastructure, and measure progress in increasing the efficiency and sustainability of water use, in particular in relation to population and economic growth. They would also recognize the different climatic environments that affect water use in countries, in particular in agriculture, which is the main user of water. Sustainability assessment is also linked to the critical thresholds fixed for this indicator and there is no universal consensus on such threshold.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTrends in water withdrawal show relatively slow patterns of change. Usually, three-five years are a minimum frequency to be able to detect significant changes, as it is unlikely that the indicator would show meaningful variations from one year to the other. Estimation of water withdrawal by sector is the main limitation to the computation of the indicator. Few countries actually publish water use data on a regular basis by sector. Renewable water resources include all surface water and groundwater resources that are available on a yearly basis without consideration of the capacity to harvest and use this resource. Exploitable water resources, which refer to the volume of surface water or groundwater that is available with an occurrence of 90% of the time, are considerably less than renewable water resources, but no universal method exists to assess such exploitable water resources. There is no universally agreed method for the computation of incoming freshwater flows originating outside of a country's borders. Nor is there any standard method to account for return flows, the part of the water withdrawn from its source and which flows back to the river system after use. In countries where return flow represents a substantial part of water withdrawal, the indicator tends to underestimate available water and therefore overestimate the level of water stress.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nOther limitations that affect the interpretation of the water stress indicator include: difficulty to obtain accurate, complete and up-to-date data; potentially large variation of sub-national data; lack of account of seasonal variations in water resources; lack of consideration to the distribution among water uses; lack of consideration of water quality and its suitability for use; and the indicator can be higher than 100 per cent when water withdrawal includes secondary freshwater (water withdrawn previously and returned to the system), non-renewable water (fossil groundwater), when annual groundwater withdrawal is higher than annual replenishment (over-abstraction) or when water withdrawal includes part or all of the water set aside for environmental water requirements. Some of these issues can be solved through disaggregation of the index at the level of hydrological units and by distinguishing between different use sectors. However, due to the complexity of water flows, both within a country and between countries, care should be taken not to double-count.", "origins": [ { "producer": "AQUASTAT - FAO's Global Information System on Water and Agriculture, FAO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529. Indicator ER.H2O.FWST.ZS (https://data.worldbank.org/indicator/ER.H2O.FWST.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.H2O.FWST.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "er_h2o_fwtl_k3": { "title": "Annual freshwater withdrawals, total (billion cubic meters)", "description_from_producer": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Annual freshwater withdrawals are total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Water withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\nStatistical concept(s):\n\n### Development relevance:\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n### Limitations and exceptions:\nA common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems.", "origins": [ { "producer": "AQUASTAT - FAO's Global Information System on Water and Agriculture, FAO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529. Indicator ER.H2O.FWTL.K3 (https://data.worldbank.org/indicator/ER.H2O.FWTL.K3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.H2O.FWTL.K3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "billion cubic meters", "display": { "unit": "billion cubic meters" } }, "er_h2o_fwtl_zs": { "title": "Annual freshwater withdrawals, total (% of internal resources)", "description_from_producer": "Annual freshwater withdrawals refer to total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where there is significant water reuse. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including withdrawals for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes. Data are for the most recent year available for 1987-2002.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Annual freshwater withdrawals are total water withdrawals, not counting evaporation losses from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Withdrawals can exceed 100 percent of total renewable resources where extraction from nonrenewable aquifers or desalination plants is considerable or where water reuse is significant. Withdrawals for agriculture and industry are total withdrawals for irrigation and livestock production and for direct industrial use (including for cooling thermoelectric plants). Withdrawals for domestic uses include drinking water, municipal use or supply, and use for public services, commercial establishments, and homes.\n\n### Development relevance:\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n### Limitations and exceptions:\nA common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.", "origins": [ { "producer": "AQUASTAT - FAO's Global Information System on Water and Agriculture, FAO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529. Indicator ER.H2O.FWTL.ZS (https://data.worldbank.org/indicator/ER.H2O.FWTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.H2O.FWTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of internal resources", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of internal resources" } }, "er_h2o_intr_k3": { "title": "Renewable internal freshwater resources, total (billion cubic meters)", "description_from_producer": "Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. Renewable water resources (internal and external) include average annual flow of rivers and recharge of aquifers generated from endogenous precipitation, and those water resources that are not generated in the country, such as inflows from upstream countries (groundwater and surface water), and part of the water of border lakes and/or rivers. Non-renewable water includes groundwater bodies (deep aquifers) that have a negligible rate of recharge on the human time-scale. While renewable water resources are expressed in flows, non-renewable water resources have to be expressed in quantity (stock). Runoff from glaciers where the mass balance is negative is considered non-renewable.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal actual renewable water resources correspond to the maximum theoretical yearly amount of water actually available for a country at a given moment. The unit of calculation is km3/year or 109 m3/year. Calculation Criteria is [Water resources: total renewable (actual)] = [Surface water: total renewable (actual)] + [Groundwater: total renewable (actual)] - [Overlap between surface water and groundwater].*\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFresh water is naturally occurring water on the Earth's surface. It is a renewable but limited natural resource. Fresh water can only be renewed through the process of the water cycle, where water from seas, lakes, forests, land, rivers, and dams evaporates, forms clouds, and returns as precipitation. However, if more fresh water is consumed through human activities than is restored by nature, the result is that the quantity of fresh water available in lakes, rivers, dams and underground waters can be reduced which can cause serious damage to the surrounding environment.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n* http://www.fao.org/nr/water/aquastat/data/glossary/search.html?termId=4188&submitBtn=s&cls=yes\n\n### Development relevance:\nUNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations.\n\n### Limitations and exceptions:\nA common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.", "origins": [ { "producer": "AQUASTAT - FAO's Global Information System on Water and Agriculture, FAO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529. Indicator ER.H2O.INTR.K3 (https://data.worldbank.org/indicator/ER.H2O.INTR.K3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.H2O.INTR.K3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "billion cubic meters", "display": { "unit": "billion cubic meters" } }, "er_h2o_intr_pc": { "title": "Renewable internal freshwater resources per capita (cubic meters)", "description_from_producer": "Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Renewable water resources (internal and external) include average annual flow of rivers and recharge of aquifers generated from endogenous precipitation, and those water resources that are not generated in the country, such as inflows from upstream countries (groundwater and surface water), and part of the water of border lakes and/or rivers. Non-renewable water includes groundwater bodies (deep aquifers) that have a negligible rate of recharge on the human time-scale. While renewable water resources are expressed in flows, non-renewable water resources have to be expressed in quantity (stock). Runoff from glaciers where the mass balance is negative is considered non-renewable. Renewable internal freshwater resources per capita are calculated using the World Bank's population estimates. The unit of calculation is m3/year per inhabitant. Internal renewable freshwater resources per capita are calculated using the World Bank's population estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTotal actual renewable water resources correspond to the maximum theoretical yearly amount of water actually available for a country at a given moment. The unit of calculation is km3/year or 10^9 m3/year. Calculation Criteria is [Water resources: total renewable (actual)] = [Surface water: total renewable (actual)] + [Groundwater: total renewable (actual)] - [Overlap between surface water and groundwater].*\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFresh water is naturally occurring water on the Earth's surface. It is a renewable but limited natural resource. Fresh water can only be renewed through the process of the water cycle, where water from seas, lakes, forests, land, rivers, and dams evaporates, forms clouds, and returns as precipitation. However, if more fresh water is consumed through human activities than is restored by nature, the result is that the quantity of fresh water available in lakes, rivers, dams and underground waters can be reduced which can cause serious damage to the surrounding environment.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n* http://www.fao.org/nr/water/aquastat/data/glossary/search.html?termId=4188&submitBtn=s&cls=yes\nStatistical concept(s):\n\n### Development relevance:\nUNESCO estimates that in developing countries in Asia, Africa and Latin America, public water withdrawal represents just 50-100 liters (13 to 26 gallons) per person per day. In regions with insufficient water resources, this figure may be as low as 20-60 (5 to 15 gallons) liters per day. People in developed countries on average consume about 10 times more water daily than those in developing countries.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWhile some countries have an abundant supply of fresh water, others do not have as much. UN estimates that many areas of the world are already experiencing stress on water availability. Due to the accelerated pace of population growth and an increase in the amount of water a single person uses, it is expected that this situation will continue to get worse. The ability of developing countries to make more water available for domestic, agricultural, industrial and environmental uses will depend on better management of water resources and more cross-sectorial planning and integration. According to World Water Council, by 2020, water use is expected to increase by 40 percent, and 17 percent more water will be required for food production to meet the needs of the growing population. The three major factors causing increasing water demand over the past century are population growth, industrial development and the expansion of irrigated agriculture.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWater productivity is an indication only of the efficiency by which each country uses its water resources. Given the different economic structure of each country, these indicators should be used carefully, taking into account a country's sectorial activities and natural resource endowments. According to Commission on Sustainable Development (CSD) agriculture accounts for more than 70 percent of freshwater drawn from lakes, rivers and underground sources. Most is used for irrigation which provides about 40 percent of the world food production. Poor management has resulted in the salinization of about 20 percent of the world's irrigated land, with an additional 1.5 million ha affected annually.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere is now ample evidence that increased hydrologic variability and change in climate has and will continue to have a profound impact on the water sector through the hydrologic cycle, water availability, water demand, and water allocation at the global, regional, basin, and local levels. Properly managed water resources are a critical component of growth, poverty reduction and equity. The livelihoods of the poorest are critically associated with access to water services. A shortage of water in the future would be detrimental to the human population as it would affect everything from sanitation, to overall health and the production of grain.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFreshwater use by continents is partly based on several socio-economic development factors, including population, physiography, and climatic characteristics. It is estimated that in the coming decades the most intensive growth of water withdrawal is expected to occur in Africa and South America (increasing by 1.5-1.6 times), while the smallest growth will take place in Europe and North America (1.2 times).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Commission for Sustainable Development (CSD) has reported that many countries lack adequate legislation and policies for efficient and equitable allocation and use of water resources. Progress is, however, being made with the review of national legislation and enactment of new laws and regulations.\n\n### Limitations and exceptions:\nA common perception is that most of the available freshwater resources are visible (on the surfaces of lakes, reservoirs and rivers). However, this visible water represents only a tiny fraction of global freshwater resources, as most of it is stored in aquifers, with the largest stocks stored in solid form in the Antarctic and in Greenland's ice cap.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on freshwater resources are based on estimates of runoff into rivers and recharge of groundwater. These estimates are based on different sources and refer to different years, so cross-country comparisons should be made with caution. Because the data are collected intermittently, they may hide significant variations in total renewable water resources from year to year. The data also fail to distinguish between seasonal and geographic variations in water availability within countries. Data for small countries and countries in arid and semiarid zones are less reliable than those for larger countries and countries with greater rainfall.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCaution should also be used in comparing data on annual freshwater withdrawals, which are subject to variations in collection and estimation methods. In addition, inflows and outflows are estimated at different times and at different levels of quality and precision, requiring caution in interpreting the data, particularly for water-short countries, notably in the Middle East and North Africa.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data are based on surveys and estimates provided by governments to the Joint Monitoring Programme of the World Health Organization (WHO) and the United Nations Children's Fund (UNICEF). The coverage rates are based on information from service users on actual household use rather than on information from service providers, which may include nonfunctioning systems.", "origins": [ { "producer": "AQUASTAT - FAO's Global Information System on Water and Agriculture, FAO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "AQUASTAT - FAO's Global Information System on Water and Agriculture, Food and Agriculture Organization of the United Nations (FAO), uri: https://data.apps.fao.org/aquastat/, publisher: Food and Agriculture Organization of the United Nations (FAO), date accessed: 20240529. Indicator ER.H2O.INTR.PC (https://data.worldbank.org/indicator/ER.H2O.INTR.PC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.H2O.INTR.PC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cubic meters", "display": { "unit": "cubic meters" } }, "er_lnd_ptld_zs": { "title": "Terrestrial protected areas (% of total land area)", "description_from_producer": "Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine areas, unclassified areas, littoral (intertidal) areas, and sites protected under local or provincial law are excluded.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon).\n\nDesignating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity.\n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\n\nThe data reported for a given year reflects all protected areas reported until January of the succeeding year. For example, the value for 2025 is calculated based on the January 2026 version of the WDPA.\n\n### Development relevance:\nThe International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTerrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Nationally protected terrestrial are terrestrial areas as a percentage of total territorial area, where all nationally designated protected areas with known location and extent are included.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestrial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n### Limitations and exceptions:\nThe data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas.\n\n### Other notes:\nRestricted use: Please contact the Protected Planet for third-party use of these data.", "origins": [ { "producer": "Protected Planet WDPA and WD-OECM data, UNEP-WCMC and IUCN, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516. Indicator ER.LND.PTLD.ZS (https://data.worldbank.org/indicator/ER.LND.PTLD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.LND.PTLD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total land area", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total land area" } }, "er_mrn_ptmr_zs": { "title": "Marine protected areas (% of territorial waters)", "description_from_producer": "Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon).\n\nDesignating an area as protected does not mean that protection is in force. And for small countries that have only protected areas smaller than 1,000 hectares, the size limit in the definition leads to an underestimate of protected areas. Nationally protected areas are defined using the six IUCN management categories for areas of at least 1,000 hectares: scientific reserves and strict nature reserves with limited public access; national parks of national or international significance and not materially affected by human activity; natural monuments and natural landscapes with unique aspects; managed nature reserves and wildlife sanctuaries; protected landscapes (which may include cultural landscapes); and areas managed mainly for the sustainable use of natural systems to ensure long-term protection and maintenance of biological diversity.\n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory per year from 1990 to present. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\n\nThe data reported for a given year reflects all protected areas reported until January of the succeeding year. For example, the value for 2025 is calculated based on the January 2026 version of the WDPA.\n\n### Development relevance:\nThe International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMarine protected areas are areas of intertidal or subtidal terrain - and overlying water and associated flora and fauna and historical and cultural features - that have been reserved by law or other effective means to protect part or the entire enclosed environment. Sites protected under local or provincial law are excluded.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestrial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n### Limitations and exceptions:\nThe data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas.\n\n### Other notes:\nRestricted use: Please contact the Protected Planet for third-party use of these data.", "origins": [ { "producer": "Protected Planet WDPA and WD-OECM data, UNEP-WCMC and IUCN, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, note: Only the latest data can be retrieved from the Protected Planet website. The time series are provided directly to WDI by Protected Planet., publisher: Protected Planet, date accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516. Indicator ER.MRN.PTMR.ZS (https://data.worldbank.org/indicator/ER.MRN.PTMR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.MRN.PTMR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of territorial waters", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of territorial waters" } }, "er_ptd_totl_zs": { "title": "Terrestrial and marine protected areas (% of total territorial area)", "description_from_producer": "Terrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine protected areas are areas of intertidal or subtidal terrain--and overlying water and associated flora and fauna and historical and cultural features--that have been reserved by law or other effective means to protect part or all of the enclosed environment. Sites protected under local or provincial law are excluded.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated using all the nationally designated protected areas recorded in the World Database on Protected Areas (WDPA) whose location and extent is known. The WDPA database is stored within a Geographic Information System (GIS) that stores information about protected areas such as their name, type and date of designation, documented area, geographic location (point) and/or boundary (polygon).\n\nA GIS analysis is used to calculate terrestrial and marine protection. For this a global protected area layer is created by combining the polygons and points recorded in the WDPA. Circular buffers are created around points based on the known extent of protected areas for which no polygon is available. Annual protected area layers are created by dissolving the global protected area layer by the known year of establishment of protected areas recorded in the WDPA. The annual protected area layers are overlaid with country/territory boundaries, coastlines and buffered coastlines (delineating the territorial waters) to obtain the absolute coverage (in square kilometers) of protected areas by country/territory per year from 1990 to present. The total area of a country's/territory's terrestrial protected areas and marine protected areas in territorial waters is divided by the total area of its land areas (including inland waters) and territorial waters to obtain the relative coverage (percentage) of protected areas.\n\nThe data reported for a given year reflects all protected areas reported until January of the succeeding year. For example, the value for 2025 is calculated based on the January 2026 version of the WDPA.\n\n### Development relevance:\nThe International Union for Conservation of Nature (IUCN) defines a protected area as \"a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTerrestrial protected areas are totally or partially protected areas of at least 1,000 hectares that are designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMarine protected areas are areas of intertidal or subtidal terrain - and overlying water and associated flora and fauna and historical and cultural features - that have been reserved by law or other effective means to protect part or the entire enclosed environment. Sites protected under local or provincial law are excluded.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAs threats to biodiversity mount, the international community is increasingly focusing on conserving diversity. Deforestation is a major cause of loss of biodiversity, and habitat conservation is vital for stemming this loss. Conservation efforts have focused on protecting areas of high biodiversity. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nProtected areas remain the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. They provide the core of efforts to protect the world's threatened species and are increasingly recognized as essential providers of ecosystem services and biological resources. Some sites are owned and managed by governments, others by private individuals, companies, communities and faith groups.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe Sustainable Development Goals (SDGs) address concerns common to all economies. In recognition of the vulnerability of animal and plant species, SDGs include targets 14 and 15 to highlight the importance of marine and terrestrial protected areas. Increasing the proportion of terrestrial and marine areas protected helps defend vulnerable plant and animal species and safeguard biodiversity.\n\n### Limitations and exceptions:\nThe data source for this indicator is the World Database on Protected Areas (WDPA), the most comprehensive global dataset on marine and terrestrial protected areas available.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe extent to which the land areas, including inland waters, and territorial waters of a country/territory are protected is useful for planning purpose to protect biodiversity. However, it is neither an indication of how well managed the terrestrial and marine protected areas are, nor confirmation that protection measures are effectively enforced. Further, the indicator does not provide information on non-designated or internationally designated protected areas that may also be important for conserving biodiversity. There are known data and knowledge gaps for some countries/regions due to difficulties in reporting national protected area data to the WDPA and/or determining whether a site conforms to the IUCN definition of a protected area.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nGaps and/or time lags in reporting national protected area data to the WDPA can however result in discrepancies, which are resolved in communication with data providers. The World Conservation Monitoring Centre (WCMC) compiles data on protected areas, numbers of certain species, and numbers of those species under threat from various sources. Because of differences in definitions, reporting practices, and reporting periods, cross-country comparability is limited.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDue to variations in consistency and methods of collection, data quality is highly variable across countries. Some countries update their information more frequently than others, some have more accurate data on extent of coverage, and many underreport the number or extent of protected areas.\n\n### Other notes:\nRestricted use: Please contact the Protected Planet for third-party use of these data.", "origins": [ { "producer": "Protected Planet WDPA and WD-OECM data, UNEP-WCMC and IUCN, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM), UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516, date published: 202405;\nInternational Union for Conservation of Nature (IUCN), uri: https://www.protectedplanet.net/en, publisher: Protected Planet, date accessed: 20240516. Indicator ER.PTD.TOTL.ZS (https://data.worldbank.org/indicator/ER.PTD.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ER.PTD.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total territorial area", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total territorial area" } }, "fb_ast_nper_zs": { "title": "Bank nonperforming loans to total gross loans (%)", "description_from_producer": "The indicator measures the proportion of a deposit taker\u2019s loan portfolio that is impaired or at risk of default. It is calculated as the ratio of non-performing loans (NPLs) to total gross loans, where NPLs are defined as loans that are past due by 90 days or more or are otherwise considered unlikely to be repaid in full without the realization of collateral. Both non-performing loans and total gross loans should be reported at their gross book value, without deducting for loan-loss provisions or collateral. This indicator provides a key measure of asset quality and potential credit risk in the banking system.\n\n### Aggregation method:\nRatio of non-performing/total loan portfolio\n\n### Statistical concept and methodology:\nMethodology: The ratio is calculated by dividing the value of non-performing loans (NPLs) by the total value of gross loans, expressed as a percentage. Both NPLs and total gross loans should be reported at their gross book value, meaning before any deductions for provisions, write-offs, or collateral. Gross loans include all outstanding loans on the balance sheet, including those classified as non-performing. The classification of NPLs should be based on either quantitative criteria (e.g., days past due) or qualitative assessments (e.g., evidence of financial difficulties), following supervisory standards aligned with Basel guidance. Data should be compiled using a consistent consolidation basis, typically the cross-border, cross-sector, domestically incorporated consolidation approach (CBCSDI), to ensure comparability across reporting entities and jurisdictions.\nStatistical concept(s): The Non-performing Loans (NPLs) to Total Gross Loans ratio is a core indicator of asset quality within the banking sector. It measures the proportion of a deposit taker\u2019s loan portfolio that is impaired or at significant risk of default. A loan is classified as non-performing when payments of principal or interest are overdue by 90 days or more, or when it is assessed that full repayment is unlikely without the realization of collateral, even if the loan is not yet past due. Data are submitted by national authorities to the IMF following the Financial Soundness Indicators (FSI) Compilation Guide.\n\n### Development relevance:\nThis indicator serves as a key measure of asset quality within the banking sector. A higher ratio indicates a greater share of impaired loans, which can signal deteriorating credit risk and financial health of lending institutions. Conversely, a lower ratio reflects better asset quality and lower credit risk. Importantly, total gross loans include the full book value of loans before provisioning and do not net out collateral or guarantees. This indicator helps assess potential vulnerabilities in a financial system, especially when tracked over time or compared across institutions and jurisdictions.\n\n### Limitations and exceptions:\nReporting countries compile the data using different methodologies, which may also vary for different points in time for the same country. Users are advised to consult the accompanying metadata on the IMF FSI website (data.imf.org) to conduct more meaningful cross-country comparisons or to assess the evolution of the indicator for any of the countries.", "origins": [ { "producer": "International Monetary Fund (Financial Soundness Indicators), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Financial Soundness Indicators, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI. Indicator FB.AST.NPER.ZS (https://data.worldbank.org/indicator/FB.AST.NPER.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FB.AST.NPER.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "fb_atm_totl_p5": { "title": "Automated teller machines (ATMs) (per 100,000 adults)", "description_from_producer": "Automated teller machines (ATMs) are electromechanical devices which enable customers of financial institutions to perform financial transactions such as cash withdrawals, balance inquiries, deposits, transfer of funds, and obtaining account information, using an electronic card.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Data are shown as the total number of ATMs for every 100,000 adults in the reporting country. Calculated as (number of ATMs)*100,000/adult population in the reporting country.\nStatistical concept(s): Data are shown as the total number of ATMs for every 100,000 adults in the reporting country. Calculated as (number of ATMs)*100,000/adult population in the reporting country.\n\n### Development relevance:\nFinancial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society\u2014especially low-income households and small enterprises\u2014have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools\u2014such as savings accounts, credit, and insurance\u2014enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society.\n\n### Limitations and exceptions:\nPopulation-based ratios of the number of branches and ATMs assume a uniform distribution of bank outlets within a country's area and across its population, while in most countries bank branches and ATMs are concentrated in urban centers of the country and are accessible only to some individuals.\n\n### Other notes:\nCountry-specific metadata can be found on the IMF\u2019s FAS website (data.imf.org).", "origins": [ { "producer": "Financial Access Survey - IMF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/?sk=E5DCAB7E-A5CA-4892-A6EA-598B5463A34C. Indicator FB.ATM.TOTL.P5 (https://data.worldbank.org/indicator/FB.ATM.TOTL.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FB.ATM.TOTL.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 adults", "display": { "unit": "per 100,000 adults" } }, "fb_bnk_capa_zs": { "title": "Bank capital to assets ratio (%)", "description_from_producer": "The indicator is a measure of capital adequacy that evaluates the financial strength of deposit takers by comparing Tier 1 capital to total assets. Tier 1 capital, often referred to as core capital, includes the most stable and readily available forms of capital, such as common equity, disclosed reserves, retained earnings, and certain other instruments that meet regulatory requirements under the Basel framework. This capital is considered the highest quality because it is fully available to cover losses and does not need to be repaid.\n\n### Statistical concept and methodology:\nMethodology: The ratio is calculated by dividing Tier 1 capital by total assets, expressed as a percentage. Tier 1 capital should be measured in accordance with the Basel regulatory framework as adopted by national supervisory authorities, ensuring consistency with international standards. Total assets are recorded at gross book value and include all financial and non-financial assets, without deduction for provisions, write-downs, or collateral. The data are compiled on a consolidated basis, typically using the cross-border, cross-sector, domestically incorporated consolidation approach (CBCSDI), which includes all relevant financial entities under common control, regardless of location or legal form.\nStatistical concept(s): The Tier 1 Capital to Assets ratio is a core financial soundness indicator that assesses the capital adequacy and financial resilience of deposit takers by measuring the proportion of high-quality, loss-absorbing capital relative to their total balance sheet size. Tier 1 capital, often referred to as core capital, comprises components such as common equity, retained earnings, and other instruments that meet Basel criteria for being fully available to cover losses.\n\n### Development relevance:\nThe size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy.\n\nTotal assets in this context refer to the gross value of all assets on the balance sheet, without deducting for provisions or reserves. By calculating the ratio of Tier 1 capital to total assets, this indicator provides a non-risk-weighted measure of leverage, offering insight into the overall capital buffer relative to the size of a deposit taker\u2019s balance sheet. A higher ratio indicates a stronger capital position and lower leverage, while a lower ratio may suggest vulnerability in times of financial stress. This metric is useful for analyzing systemic stability and comparing institutions that may have different levels of risk exposure.\n\n### Limitations and exceptions:\nReporting countries compile the data using different methodologies, which may also vary for different points in time for the same country. Users are advised to consult the accompanying metadata on the IMF FSI website (data.imf.org) to conduct more meaningful cross-country comparisons or to assess the evolution of the indicator for any of the countries.", "origins": [ { "producer": "International Monetary Fund (Financial Soundness Indicators), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Financial Soundness Indicators, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI. Indicator FB.BNK.CAPA.ZS (https://data.worldbank.org/indicator/FB.BNK.CAPA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FB.BNK.CAPA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "fb_cbk_brch_p5": { "title": "Commercial bank branches (per 100,000 adults)", "description_from_producer": "Commercial bank branches are retail locations of resident commercial banks and other resident banks that function as commercial banks that provide financial services to customers and are physically separated from the main office but not organized as legally separated subsidiaries.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Number of branches refers to all units in the country that are physically separated from the main offices, but not incorporated as separate subsidiaries. The number of main offices is excluded. Typically, a branch provides a wide range of services to its customers including cash withdrawals, deposits in an account with a teller, financial advice, safe deposit box rentals, and currency exchange. Units, with or without human staff, which offer only automated services for cash withdrawal and deposits, or computer terminals for online banking and check depositing machines should also be counted as branches.\nStatistical concept(s): Data are shown as the number of branches of commercial banks for every 100,000 adults in the reporting country. It is calculated as: (Number of branches/Adult population)*100,000\n\n### Development relevance:\nFinancial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society\u2014especially low-income households and small enterprises\u2014have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools\u2014such as savings accounts, credit, and insurance\u2014enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society.\n\n### Limitations and exceptions:\nPopulation-based ratios of the number of branches and ATMs assume a uniform distribution of bank outlets within a country's area and across its population, while in most countries bank branches and ATMs are concentrated in urban centers of the country and are accessible only to some individuals.\n\n### Other notes:\nCountry-specific metadata can be found on the IMF\u2019s FAS website (data.imf.org).", "origins": [ { "producer": "IMF Financial Access Survey, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF.STA:FAS. Indicator FB.CBK.BRCH.P5 (https://data.worldbank.org/indicator/FB.CBK.BRCH.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FB.CBK.BRCH.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 adults", "display": { "unit": "per 100,000 adults" } }, "fb_cbk_brwr_p3": { "title": "Borrowers from commercial banks (per 1,000 adults)", "description_from_producer": "Borrowers from commercial banks are the reported number of resident customers that are nonfinancial corporations (public and private) and households who obtained loans from commercial banks and other banks functioning as commercial banks.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Number of borrowers refers to the number of resident nonfinancial corporations (public and private) and individuals in the household sector that have obtained credit (loans) from each type of financial institution.\n\n\u2022 A corporate entity must be counted as one borrower, irrespective of the number of loans extended to that corporate borrower.\n\u2022 An individual from the household sector must be counted as one borrower, irrespective of the number of loan accounts held.\n\u2022 If a loan is extended to a group of borrowers, all borrowers must be counted individually rather than as one borrower.\n\nStatistical concept(s): Borrowers from commercial banks denotes the total number of resident customers that are nonfinancial corporations (public and private) and households who obtained loans from commercial banks for every 1,000 adults in the reporting country. It is calculated as (number of borrowers)*1,000/adult population in the reporting country.\n\n### Development relevance:\nFinancial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society\u2014especially low-income households and small enterprises\u2014have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools\u2014such as savings accounts, credit, and insurance\u2014enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society.\n\n### Limitations and exceptions:\nFor many countries data cover the total number of loan accounts due to lack of information on loan account holders. For several countries, data cover all borrowers including commercial banks, credit unions and financial cooperatives, deposit taking microfinance institutions, and other deposit takers. These include all resident financial corporations and quasi-corporations (except the central bank) that are mainly engaged in financial intermediation and that issue liabilities included in the national definition of broad money. These institutions have varying names in different countries, such as savings and loan associations, building societies, rural banks and agricultural banks, post office giro institutions, post office savings banks, savings banks, and money market funds.\n\n### Other notes:\nCountry-specific metadata can be found on the IMF\u2019s FAS website (data.imf.org).", "origins": [ { "producer": "IMF Financial Access Survey, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF.STA:FAS. Indicator FB.CBK.BRWR.P3 (https://data.worldbank.org/indicator/FB.CBK.BRWR.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FB.CBK.BRWR.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 adults", "display": { "unit": "per 1,000 adults" } }, "fb_cbk_dptr_p3": { "title": "Depositors with commercial banks (per 1,000 adults)", "description_from_producer": "Depositors with commercial banks are the reported number of deposit account holders, including both resident non-financial corporations (both public and private) and individuals from the household sector, at commercial banks within the reporting jurisdiction for every 1,000 adults. The major types of deposits are checking accounts, savings accounts, and time deposits.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: While calculating the number of depositors at each type of financial institution, the following are recommended: (a) Corporate accounts are counted as only one depositor, irrespective of the number of deposit accounts (checking, demand, saving, time deposits, etc.) held; (b) Individual accounts are counted as only one depositor, irrespective of the number of deposit accounts (checking, demand, saving, time deposits, etc.) held; (c) For joint accounts, all depositors are counted individually rather than as one depositor.\nStatistical concept(s): Depositors with commercial banks are deposit account holders at commercial banks and other resident banks functioning as commercial banks that are resident nonfinancial corporations (public and private) and households. It is calculated as (number of depositors)*1,000/adult population in the reporting country. The major types of deposits are checking accounts, savings accounts, and time deposits.\n\n### Development relevance:\nFinancial inclusion acts as a powerful driver not only of economic growth but, more importantly, of inclusive growth. It ensures that diverse segments of society\u2014especially low-income households and small enterprises\u2014have access to and can effectively use financial services, allowing them to share in the benefits of economic development. By promoting savings and investment, stabilizing consumption, and reducing the financial vulnerability of individuals and businesses, financial inclusion supports broader economic expansion. Accessible and affordable financial tools\u2014such as savings accounts, credit, and insurance\u2014enable people, particularly those traditionally underserved or excluded, to invest in their futures, manage spending more effectively, and mitigate financial risks. These benefits can translate into higher income levels and contribute to reducing poverty and inequality. Ultimately, financial inclusion aims to expand economic opportunity and participation, helping to build a more equitable and prosperous society.\n\n### Limitations and exceptions:\nFor many countries data cover the total number of deposit accounts due to lack of information on account holders.\n\n### Other notes:\nCountry-specific metadata can be found on the IMF\u2019s FAS website (data.imf.org).", "origins": [ { "producer": "IMF Financial Access Survey, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Financial Access Survey, International Monetary Fund (IMF), uri: https://data.imf.org/en/datasets/IMF.STA:FAS. Indicator FB.CBK.DPTR.P3 (https://data.worldbank.org/indicator/FB.CBK.DPTR.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FB.CBK.DPTR.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 adults", "display": { "unit": "per 1,000 adults" } }, "fd_ast_prvt_gd_zs": { "title": "Domestic credit to private sector by banks (% of GDP)", "description_from_producer": "Domestic credit to private sector by banks refers to financial resources provided to the private sector by other depository corporations (deposit taking corporations except central banks), such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related to monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.\n\n### Limitations and exceptions:\nCredit to the private sector may sometimes include credit to state-owned or partially state-owned enterprises.", "origins": [ { "producer": "International Monetary Fund, World Bank, and OECD", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD). Indicator FD.AST.PRVT.GD.ZS (https://data.worldbank.org/indicator/FD.AST.PRVT.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FD.AST.PRVT.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "fd_res_liqu_as_zs": { "title": "Bank liquid reserves to bank assets ratio (%)", "description_from_producer": "Ratio of bank liquid reserves to bank assets is the ratio of domestic currency holdings and deposits with the monetary authorities to claims on other governments, nonfinancial public enterprises, the private sector, and other banking institutions. This indicator is expressed as a percentage (a\u00f7b)*100.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FD.RES.LIQU.AS.ZS (https://data.worldbank.org/indicator/FD.RES.LIQU.AS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FD.RES.LIQU.AS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "fi_res_totl_cd": { "title": "Total reserves (includes gold, current US$)", "description_from_producer": "Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related to the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.\n\n### Limitations and exceptions:\nDiscrepancies may arise in the balance of payments because there is no single source for balance of payments data and therefore no way to ensure that the data are fully consistent. Sources include customs data, monetary accounts of the banking system, external debt records, information provided by enterprises, surveys to estimate service transactions, and foreign exchange records. Differences in collection methods - such as in timing, definitions of residence and ownership, and the exchange rate used to value transactions - contribute to net errors and omissions. In addition, smuggling and other illegal or quasi-legal transactions may be unrecorded or misrecorded.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FI.RES.TOTL.CD (https://data.worldbank.org/indicator/FI.RES.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FI.RES.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "includes gold, current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "includes gold, current US$" } }, "fi_res_totl_dt_zs": { "title": "Total reserves (% of total external debt)", "description_from_producer": "Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed as a percentage of total external debt which are all liabilities that require payment(s) of interest and/or principal by the debtor at some point(s) in the future and that are owed to non-residents by residents of an economy.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related to the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Debt Statistics - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Debt Statistics, World Bank (WB). Indicator FI.RES.TOTL.DT.ZS (https://data.worldbank.org/indicator/FI.RES.TOTL.DT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FI.RES.TOTL.DT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total external debt", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total external debt" } }, "fi_res_totl_mo": { "title": "Total reserves in months of imports", "description_from_producer": "Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This item is expressed in terms of the number of months of imports of goods and services they could pay for [X/(Imports/12)].\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related to monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FI.RES.TOTL.MO (https://data.worldbank.org/indicator/FI.RES.TOTL.MO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FI.RES.TOTL.MO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "fi_res_xgld_cd": { "title": "Total reserves minus gold (current US$)", "description_from_producer": "This series includes external assets (excluding monetary gold) that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related to the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FI.RES.XGLD.CD (https://data.worldbank.org/indicator/FI.RES.XGLD.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FI.RES.XGLD.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "fm_ast_cgov_zg_m3": { "title": "Claims on central government (annual growth as % of broad money)", "description_from_producer": "Claims on central government include loans to central government institutions net of deposits. Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator represents the annual percentage growth in the ratio of claims to broad money.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related to the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.\n\n### Limitations and exceptions:\nMonetary accounts are derived from the balance sheets of financial institutions - the central bank, commercial banks, and nonbank financial intermediaries. Although these balance sheets are usually reliable, they are subject to errors of classification, valuation, and timing and to differences in accounting practices. For example, whether interest income is recorded on an accrual or a cash basis can make a substantial difference, as can the treatment of nonperforming assets. Valuation errors typically arise for foreign exchange transactions, particularly in countries with flexible exchange rates or in countries that have undergone currency devaluation during the reporting period. The valuation of financial derivatives and the net liabilities of the banking system can also be difficult. The quality of commercial bank reporting also may be adversely affected by delays in reports from bank branches, especially in countries where branch accounts are not computerized. Thus the data in the balance sheets of commercial banks may be based on preliminary estimates subject to constant revision. This problem is likely to be even more serious for nonbank financial intermediaries.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FM.AST.CGOV.ZG.M3 (https://data.worldbank.org/indicator/FM.AST.CGOV.ZG.M3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FM.AST.CGOV.ZG.M3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual growth as % of broad money", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual growth as % of broad money" } }, "fm_ast_domo_zg_m3": { "title": "Claims on other sectors of the domestic economy (annual growth as % of broad money)", "description_from_producer": "Claims on other sectors of the domestic economy include gross credit from the financial system to households, nonprofit institutions serving households, nonfinancial corporations, state and local governments, and social security funds. Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator represents the annual percentage growth in the ratio of claims to broad money.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.\n\n### Limitations and exceptions:\nMonetary accounts are derived from the balance sheets of financial institutions - the central bank, commercial banks, and nonbank financial intermediaries. Although these balance sheets are usually reliable, they are subject to errors of classification, valuation, and timing and to differences in accounting practices. For example, whether interest income is recorded on an accrual or a cash basis can make a substantial difference, as can the treatment of nonperforming assets. Valuation errors typically arise for foreign exchange transactions, particularly in countries with flexible exchange rates or in countries that have undergone currency devaluation during the reporting period. The valuation of financial derivatives and the net liabilities of the banking system can also be difficult. The quality of commercial bank reporting also may be adversely affected by delays in reports from bank branches, especially in countries where branch accounts are not computerized. Thus the data in the balance sheets of commercial banks may be based on preliminary estimates subject to constant revision. This problem is likely to be even more serious for nonbank financial intermediaries.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FM.AST.DOMO.ZG.M3 (https://data.worldbank.org/indicator/FM.AST.DOMO.ZG.M3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FM.AST.DOMO.ZG.M3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual growth as % of broad money", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual growth as % of broad money" } }, "fm_ast_doms_cn": { "title": "Net domestic credit (current LCU)", "description_from_producer": "Net domestic credit is the sum of net claims on the central government and claims on other sectors of the domestic economy. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FM.AST.DOMS.CN (https://data.worldbank.org/indicator/FM.AST.DOMS.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FM.AST.DOMS.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "fm_ast_nfrg_cn": { "title": "Net foreign assets (current LCU)", "description_from_producer": "Net foreign assets are the sum of foreign assets held by monetary authorities and deposit money banks, less their foreign liabilities. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FM.AST.NFRG.CN (https://data.worldbank.org/indicator/FM.AST.NFRG.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FM.AST.NFRG.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "fm_ast_prvt_gd_zs": { "title": "Monetary Sector credit to private sector (% GDP)", "description_from_producer": "Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Monetary Fund, World Bank, and OECD", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD). Indicator FM.AST.PRVT.GD.ZS (https://data.worldbank.org/indicator/FM.AST.PRVT.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FM.AST.PRVT.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "fm_ast_prvt_zg_m3": { "title": "Claims on private sector (annual growth as % of broad money)", "description_from_producer": "Claims on private sector include gross credit from the financial system to individuals, enterprises, nonfinancial public entities not included under net domestic credit, and financial institutions not included elsewhere. Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator represents the annual percentage growth in the ratio of claims to broad money.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FM.AST.PRVT.ZG.M3 (https://data.worldbank.org/indicator/FM.AST.PRVT.ZG.M3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FM.AST.PRVT.ZG.M3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual growth as % of broad money", "short_unit": "%", "display": { "conversionFactor": 100, "shortUnit": "%", "unit": "annual growth as % of broad money" } }, "fm_lbl_bmny_cn": { "title": "Broad money (current LCU)", "description_from_producer": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FM.LBL.BMNY.CN (https://data.worldbank.org/indicator/FM.LBL.BMNY.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FM.LBL.BMNY.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "fm_lbl_bmny_gd_zs": { "title": "Broad money (% of GDP)", "description_from_producer": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related to the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.\n\n### Other notes:\nThe derivation of this indicator was simplified in September 2012 to be current-year broad money divided by current-year GDP times 100.", "origins": [ { "producer": "International Monetary Fund, World Bank, and OECD", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD). Indicator FM.LBL.BMNY.GD.ZS (https://data.worldbank.org/indicator/FM.LBL.BMNY.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FM.LBL.BMNY.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "fm_lbl_bmny_ir_zs": { "title": "Broad money to total reserves ratio", "description_from_producer": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. Reserve assets are external assets, including monetary gold, that are readily available to and controlled by monetary authorities for meeting balance of payments financing needs, for intervention in exchange markets to affect the currency exchange rate, and for other related purposes (such as maintaining confidence in the currency and the economy, and serving as a basis for foreign borrowing). Reserve assets must be denominated and settled in foreign currency. This indicator is expressed as a ratio (a\u00f7b).\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FM.LBL.BMNY.IR.ZS (https://data.worldbank.org/indicator/FM.LBL.BMNY.IR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FM.LBL.BMNY.IR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "fm_lbl_bmny_zg": { "title": "Broad money growth (annual %)", "description_from_producer": "Broad money is the sum of all liquid financial instruments held by money-holding sectors that are widely accepted in an economy as a medium of exchange, plus those that can be converted into a medium of exchange at short notice at, or close to, their full nominal value. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.\n\n### Limitations and exceptions:\nMonetary accounts are derived from the balance sheets of financial institutions - the central bank, commercial banks, and nonbank financial intermediaries. Although these balance sheets are usually reliable, they are subject to errors of classification, valuation, and timing and to differences in accounting practices. For example, whether interest income is recorded on an accrual or a cash basis can make a substantial difference, as can the treatment of nonperforming assets. Valuation errors typically arise for foreign exchange transactions, particularly in countries with flexible exchange rates or in countries that have undergone currency devaluation during the reporting period. The valuation of financial derivatives and the net liabilities of the banking system can also be difficult. The quality of commercial bank reporting also may be adversely affected by delays in reports from bank branches, especially in countries where branch accounts are not computerized. Thus the data in the balance sheets of commercial banks may be based on preliminary estimates subject to constant revision. This problem is likely to be even more serious for nonbank financial intermediaries.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FM.LBL.BMNY.ZG (https://data.worldbank.org/indicator/FM.LBL.BMNY.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FM.LBL.BMNY.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" } }, "fp_cpi_totl": { "title": "Consumer price index (2010 = 100)", "description_from_producer": "Index of the prices of consumption goods and services, as compared to a certain reference period (2010=100).\n\n### Statistical concept and methodology:\nMethodology: Consumer Prices Indices are compiled in accordance with international standards: Consumer Price Index Manual, 2020 or 2004 version. Specific information on how countries compile their CPI statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of a consumer price index series is to measure the rate at which prices of consumption goods and services are changing from one period to another.\n\n### Development relevance:\nA general and continuing increase in an economy\u2019s price level is called inflation. The increase in the average prices of goods and services in the economy should be distinguished from a change in the relative prices of individual goods and services. Generally accompanying an overall increase in the price level is a change in the structure of relative prices, but it is only the average increase, not the relative price changes, that constitutes inflation. A commonly used measure of inflation is the consumer price index, which measures the prices of a representative basket of goods and services purchased by a typical household. The consumer price index is usually calculated on the basis of periodic surveys of consumer prices. Other price indices are derived implicitly from indexes of current and constant price series.\n\n### Limitations and exceptions:\nConsumer price indexes should be interpreted with caution. The definition of a household, the basket of goods, and the geographic (urban or rural) and income group coverage of consumer price surveys can vary widely by country. In addition, weights are derived from household expenditure surveys, which, for budgetary reasons, tend to be conducted infrequently in developing countries, impairing comparability over time. Although useful for measuring consumer price inflation within a country, consumer price indexes are of less value in comparing countries.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FP.CPI.TOTL (https://data.worldbank.org/indicator/FP.CPI.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FP.CPI.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2010 = 100", "display": { "unit": "2010 = 100" } }, "fp_cpi_totl_zg": { "title": "Inflation, consumer prices (annual %)", "description_from_producer": "Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Consumer Prices Indices are compiled in accordance with international standards: Consumer Price Index Manual, 2020 or 2004 version. Specific information on how countries compile their CPI statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of a consumer price index series is to measure the rate at which prices of consumption goods and services are changing from one period to another.\n\n### Development relevance:\nA general and continuing increase in an economy\u2019s price level is called inflation. The increase in the average prices of goods and services in the economy should be distinguished from a change in the relative prices of individual goods and services. Generally accompanying an overall increase in the price level is a change in the structure of relative prices, but it is only the average increase, not the relative price changes, that constitutes inflation. A commonly used measure of inflation is the consumer price index, which measures the prices of a representative basket of goods and services purchased by a typical household. The consumer price index is usually calculated on the basis of periodic surveys of consumer prices. Other price indices are derived implicitly from indexes of current and constant price series.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FP.CPI.TOTL.ZG (https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" } }, "fp_wpi_totl": { "title": "Wholesale price index (2010 = 100)", "description_from_producer": "Index of prices of a mix of agricultural and industrial goods at various stages of production and distribution, including import duties, as compared to a certain reference period (2010=100).\n\n### Statistical concept and methodology:\nMethodology: Wholesale Prices Indices are compiled in accordance with international standards: Producer Price Index Manual, 2004 version. Specific information on how countries compile their WPI statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of a wholesale price index series is to measure the rate at which prices of goods and services are changing from one period to another, for products that flow from a wholesaler to a retailer.\n\n### Development relevance:\nA general and continuing increase in an economy\u2019s price level is called inflation. The increase in the average prices of goods and services in the economy should be distinguished from a change in the relative prices of individual goods and services. Generally accompanying an overall increase in the price level is a change in the structure of relative prices, but it is only the average increase, not the relative price changes, that constitutes inflation.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FP.WPI.TOTL (https://data.worldbank.org/indicator/FP.WPI.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FP.WPI.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2010 = 100", "display": { "unit": "2010 = 100" } }, "fr_inr_dpst": { "title": "Deposit interest rate (%)", "description_from_producer": "Deposit interest rate is the rate paid by commercial or similar banks for demand, time, or savings deposits. The terms and conditions attached to these rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a\u00f7b)*100.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nBoth banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy.\n\n### Limitations and exceptions:\nCountries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FR.INR.DPST (https://data.worldbank.org/indicator/FR.INR.DPST). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FR.INR.DPST", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "fr_inr_lend": { "title": "Lending interest rate (%)", "description_from_producer": "Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nBoth banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy.\n\n### Limitations and exceptions:\nCountries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FR.INR.LEND (https://data.worldbank.org/indicator/FR.INR.LEND). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FR.INR.LEND", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "fr_inr_lndp": { "title": "Interest rate spread (lending rate minus deposit rate, %)", "description_from_producer": "Interest rate spread is the interest rate charged by banks on loans to private sector customers minus the interest rate paid by commercial or similar banks for demand, time, or savings deposits. The terms and conditions attached to these rates differ by country, however, limiting their comparability.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nBoth banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy.\n\n### Limitations and exceptions:\nCountries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FR.INR.LNDP (https://data.worldbank.org/indicator/FR.INR.LNDP). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FR.INR.LNDP", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "lending rate minus deposit rate, %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "lending rate minus deposit rate, %" } }, "fr_inr_rinr": { "title": "Real interest rate (%)", "description_from_producer": "An interest rate is the amount charged, expressed as a percentage of the principal over a period of time, by the owners of certain kinds of financial assets for putting the financial assets at the disposal of another institutional unit. The real interest rate is the lending interest rate adjusted for inflation as measured by the GDP deflator. The terms and conditions attached to lending rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a\u00f7b)*100.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nBoth banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy.", "origins": [ { "producer": "IMF International Financial Statistics and World Bank World Development Indicators", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators, World Bank (WB). Indicator FR.INR.RINR (https://data.worldbank.org/indicator/FR.INR.RINR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FR.INR.RINR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "fr_inr_risk": { "title": "Risk premium on lending (lending rate minus treasury bill rate, %)", "description_from_producer": "Risk premium on lending is the interest rate charged by banks on loans to private sector customers minus the \"risk free\" treasury bill interest rate at which short-term government securities are issued or traded in the market. In some countries this spread may be negative, indicating that the market considers its best corporate clients to be lower risk than the government. The terms and conditions attached to lending rates differ by country, however, limiting their comparability.\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nBoth banking and financial systems enhance growth, the main factor in poverty reduction. At low levels of economic development commercial banks tend to dominate the financial system, while at higher levels domestic stock markets tend to become more active and efficient. The size and mobility of international capital flows make it increasingly important to monitor the strength of financial systems. Robust financial systems can increase economic activity and welfare, but instability can disrupt financial activity and impose widespread costs on the economy.\n\n### Limitations and exceptions:\nCountries use a variety of reporting formats, sample designs, interest compounding formulas, averaging methods, and data presentations for indices and other data series on interest rates. The IMF's Monetary and Financial Statistics Manual does not provide guidelines beyond the general recommendation that such data should reflect market prices and effective (rather than nominal) interest rates and should be representative of the financial assets and markets to be covered. For more information, please see http://www.imf.org/external/pubs/ft/mfs/manual/index.htm.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator FR.INR.RISK (https://data.worldbank.org/indicator/FR.INR.RISK). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FR.INR.RISK", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "lending rate minus treasury bill rate, %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "lending rate minus treasury bill rate, %" } }, "fs_ast_cgov_gd_zs": { "title": "Claims on central government, etc. (% GDP)", "description_from_producer": "Claims on central government include loans to central government institutions net of deposits. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistic Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Monetary Fund, World Bank, and OECD", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD). Indicator FS.AST.CGOV.GD.ZS (https://data.worldbank.org/indicator/FS.AST.CGOV.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FS.AST.CGOV.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% GDP" } }, "fs_ast_domo_gd_zs": { "title": "Claims on other sectors of the domestic economy (% of GDP)", "description_from_producer": "Claims on other sectors of the domestic economy include gross credit from the financial system to households, nonprofit institutions serving households, nonfinancial corporations, state and local governments, and social security funds. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.", "origins": [ { "producer": "International Monetary Fund, World Bank, and OECD", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD). Indicator FS.AST.DOMO.GD.ZS (https://data.worldbank.org/indicator/FS.AST.DOMO.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FS.AST.DOMO.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "fs_ast_doms_gd_zs": { "title": "Domestic credit provided by financial sector (% of GDP)", "description_from_producer": "Domestic credit provided by the financial sector includes all credit to various sectors on a gross basis, with the exception of credit to the central government, which is net. The financial sector includes monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.\n\n### Limitations and exceptions:\nIn a few countries governments may hold international reserves as deposits in the banking system rather than in the central bank. Since claims on the central government are a net item (claims on the central government minus central government deposits), the figure may be negative, resulting in a negative figure for domestic credit provided by the banking sector.", "origins": [ { "producer": "International Monetary Fund, World Bank, and OECD", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD). Indicator FS.AST.DOMS.GD.ZS (https://data.worldbank.org/indicator/FS.AST.DOMS.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FS.AST.DOMS.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "fs_ast_prvt_gd_zs": { "title": "Domestic credit to private sector (% of GDP)", "description_from_producer": "Domestic credit to private sector refers to financial resources provided to the private sector by financial corporations, such as through loans, purchases of nonequity securities, and trade credits and other accounts receivable, that establish a claim for repayment. For some countries these claims include credit to public enterprises. The financial corporations include monetary authorities and deposit money banks, as well as other financial corporations where data are available (including corporations that do not accept transferable deposits but do incur such liabilities as time and savings deposits). Examples of other financial corporations are finance and leasing companies, money lenders, insurance corporations, pension funds, and foreign exchange companies. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Financial statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual framework comes from the Monetary and Financial Statistics Manual which outlines the analytical presentation of monetary statistics, which provide critical inputs for monetary policy formulation and monitoring. The statistics covered in this Manual also support the assessment of financial system stability.\n\n### Development relevance:\nThis indicator is related the monetary and financial statistics. Monetary and financial statistics are crucial as they offer a detailed picture of a country's financial condition and the workings of its monetary system. This encompasses information on the money supply, prevailing interest rates, and the activities of financial institutions. Central banks and policymakers rely on these statistics to craft monetary policy, manage interest rates, and regulate inflation. For investors and market analysts, these figures provide a window into the financial sector's stability and performance, guiding investment decisions and risk evaluations. They also shed light on the circulation of money within the economy, which has direct implications for consumer spending, business investments, and the overall trajectory of economic growth. Ultimately, these statistics play a pivotal role in ensuring economic stability and promoting sustainable development.\n\n### Limitations and exceptions:\nCredit to the private sector may sometimes include credit to state-owned or partially state-owned enterprises.", "origins": [ { "producer": "International Monetary Fund, World Bank, and OECD", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF);\nWorld Development Indicators Database, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD). Indicator FS.AST.PRVT.GD.ZS (https://data.worldbank.org/indicator/FS.AST.PRVT.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FS.AST.PRVT.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "fx_own_totl_40_zs": { "title": "Account ownership at a financial institution or with a mobile-money-service provider, poorest 40% (% of population ages 15+)", "description_from_producer": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (poorest 40%, share of population ages 15+).\n\n### Periodicity:\nTriennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (poorest 40%, share of population ages 15+).\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (poorest 40%, share of population ages 15+).\n\n### Development relevance:\nG20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families. financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). https://www.sciencedirect.com/science/article/pii/S2110701724000027\n\n### Other notes:\nEach economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022).", "origins": [ { "producer": "Global Findex Database - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex. Indicator FX.OWN.TOTL.40.ZS (https://data.worldbank.org/indicator/FX.OWN.TOTL.40.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FX.OWN.TOTL.40.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population ages 15+", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population ages 15+" } }, "fx_own_totl_60_zs": { "title": "Account ownership at a financial institution or with a mobile-money-service provider, richest 60% (% of population ages 15+)", "description_from_producer": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+).\n\n### Periodicity:\nTriennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+).\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (richest 60%, share of population ages 15+).\n\n### Development relevance:\nFinancial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for Financial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirg\u00fc\u00e7-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda(GPFI, 2011).\n\nTherefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others).\n\nFinancial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027\n\n### Other notes:\nEach economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022).", "origins": [ { "producer": "Global Findex Database - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex. Indicator FX.OWN.TOTL.60.ZS (https://data.worldbank.org/indicator/FX.OWN.TOTL.60.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FX.OWN.TOTL.60.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population ages 15+", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population ages 15+" } }, "fx_own_totl_fe_zs": { "title": "Account ownership at a financial institution or with a mobile-money-service provider, female (% of population ages 15+)", "description_from_producer": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (female, % age 15+).\n\n### Periodicity:\nTriennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (female, % age 15+). Assessment of this occurs triennial\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (female, % age 15+). Assessment of this occurs triennial\n\n### Development relevance:\nFinancial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirg\u00fc\u00e7-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families.\n\nIn addition, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027\n\n### Other notes:\nEach economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022).", "origins": [ { "producer": "Global Findex Database - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex. Indicator FX.OWN.TOTL.FE.ZS (https://data.worldbank.org/indicator/FX.OWN.TOTL.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FX.OWN.TOTL.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population ages 15+", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population ages 15+" } }, "fx_own_totl_ma_zs": { "title": "Account ownership at a financial institution or with a mobile-money-service provider, male (% of population ages 15+)", "description_from_producer": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+).\n\n### Periodicity:\nTriennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+). Assessment of this occurs triennial.\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+). Assessment of this occurs triennial.\n\n### Development relevance:\nfinancial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination.\n\nFinancial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirg\u00fc\u00e7-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families.\nhttps://www.sciencedirect.com/science/article/pii/S2110701724000027\n\n### Other notes:\nEach economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022).", "origins": [ { "producer": "Global Findex Database - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex. Indicator FX.OWN.TOTL.MA.ZS (https://data.worldbank.org/indicator/FX.OWN.TOTL.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FX.OWN.TOTL.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population ages 15+", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population ages 15+" } }, "fx_own_totl_ol_zs": { "title": "Account ownership at a financial institution or with a mobile-money-service provider, older adults (% of population ages 25+)", "description_from_producer": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (older adults, % of population ages 25+).\n\n### Periodicity:\nTriennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (older adults, % of population ages 25+). This assessment occurs triennially\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (older adults, % of population ages 25+). This assessment occurs triennially\n\n### Development relevance:\nFinancial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirg\u00fc\u00e7-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families\n\nfinancial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027\n\n### Other notes:\nEach economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022).", "origins": [ { "producer": "Global Findex Database - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex. Indicator FX.OWN.TOTL.OL.ZS (https://data.worldbank.org/indicator/FX.OWN.TOTL.OL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FX.OWN.TOTL.OL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population ages 25+", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population ages 25+" } }, "fx_own_totl_pl_zs": { "title": "Account ownership at a financial institution or with a mobile-money-service provider, primary education or less (% of population ages 15+)", "description_from_producer": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+).\n\n### Periodicity:\nTriennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+). This assessment is conducted triennially\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (primary education or less, % of population ages 15+). This assessment is conducted triennially\n\n### Development relevance:\nFinancial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirg\u00fc\u00e7-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that it offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination.\n\n### Other notes:\nEach economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022).", "origins": [ { "producer": "Global Findex Database - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex. Indicator FX.OWN.TOTL.PL.ZS (https://data.worldbank.org/indicator/FX.OWN.TOTL.PL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FX.OWN.TOTL.PL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population ages 15+", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population ages 15+" } }, "fx_own_totl_so_zs": { "title": "Account ownership at a financial institution or with a mobile-money-service provider, secondary education or more (% of population ages 15+)", "description_from_producer": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+).\n\n### Periodicity:\nTriennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+). This assessment is conducted triennially\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+). This assessment is conducted triennially\n\n### Development relevance:\nFinancial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirg\u00fc\u00e7-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination. https://www.sciencedirect.com/science/article/pii/S2110701724000027\n\n### Other notes:\nEach economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022).", "origins": [ { "producer": "Global Findex Database - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex. Indicator FX.OWN.TOTL.SO.ZS (https://data.worldbank.org/indicator/FX.OWN.TOTL.SO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FX.OWN.TOTL.SO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population ages 15+", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population ages 15+" } }, "fx_own_totl_yg_zs": { "title": "Account ownership at a financial institution or with a mobile-money-service provider, young adults (% of population ages 15-24)", "description_from_producer": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24).\n\n### Periodicity:\nTriennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24). This assessment is conducted triennially.\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24). This assessment is conducted triennially.\n\n### Development relevance:\nFinancial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirg\u00fc\u00e7-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination (Levine et al., 2000; Honohan, 2004; DFID, 2004; Levine, 2004; Andrianova and Demetriades, 2008). https://www.sciencedirect.com/science/article/pii/S2110701724000027\n\n### Other notes:\nEach economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022).", "origins": [ { "producer": "Global Findex Database - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex. Indicator FX.OWN.TOTL.YG.ZS (https://data.worldbank.org/indicator/FX.OWN.TOTL.YG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FX.OWN.TOTL.YG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population ages 15-24", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population ages 15-24" } }, "fx_own_totl_zs": { "title": "Account ownership at a financial institution or with a mobile-money-service provider (% of population ages 15+)", "description_from_producer": "Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+).\n\n### Periodicity:\nTriennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+). Assessment conducted triennially\nStatistical concept(s): Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+). Assessment conducted triennially\n\n### Development relevance:\nFinancial inclusion allows individuals and firms to take advantage of business opportunities, invest in education, save for retirement, and insure against risks (Demirg\u00fc\u00e7-Kunt et al., 2008). At the G20 Summit in 2010 held in Seoul, South Korea, financial inclusion was recognized as one of the nine key pillars of the global development agenda (GPFI, 2011). Therefore, financial inclusion is a key pillar to country development since financial inclusion ensures that everyone benefits from banking services and help to eradicate poverty and reduce inequality. In this sense, financial inclusion should be understood as the coexistence of a variety of formal financial services, offered at a fair price, in the right place, in the form and time required, and without inequity to all agents of the economy, especially for at-risk groups such as unprotected segments and low-income families (see, for example, Agarwal, 2010; Hannig and Stefan, 2010; Sarma and Pais, 2011; Kumar, 2013; Ghosh and Dixit, 2014; Talledo, 2015; Aparicio et al., 2016; Schmied and Marr, 2016; among others). In addition, nowadays, financial inclusion is a key pillar to green finance since sustainable development is the path way to the future in the way that if offers a framework to increase the levels of per capita GDP. In this line, financial development and economic growth have received considerable attention across recent decades (Levine et al., 2000; Bruce et al., 2013), and there is consensus around the positive effect of financial variables on economic growth (Levine, 2005). Over time, the position of the financial sector in relation to economic growth has generated increasing research, with the literature generally focused on economic growth as associated with domestic savings, capital accumulation, technological innovation, income growth, and financial determination (Levine et al., 2000; Honohan, 2004; DFID, 2004; Levine, 2004; Andrianova and Demetriades, 2008). https://www.sciencedirect.com/science/article/pii/S2110701724000027\n\n### Other notes:\nEach economy is classified based on the classification of World Bank Group's fiscal year 2022 (July 1, 2021-June 30, 2022).", "origins": [ { "producer": "Global Findex Database - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Findex Database, World Bank (WB), uri: https://www.worldbank.org/en/publication/globalfindex. Indicator FX.OWN.TOTL.ZS (https://data.worldbank.org/indicator/FX.OWN.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/FX.OWN.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% age 15+", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% age 15+" } }, "gb_xpd_rsdv_gd_zs": { "title": "Research and development expenditure (% of GDP)", "description_from_producer": "Gross domestic expenditures on research and development (R&D), expressed as a percent of GDP. They include both capital and current expenditures in the four main sectors: Business enterprise, Government, Higher education and Private non-profit. R&D covers basic research, applied research, and experimental development.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing the total domestic intramural expenditure on R&D for a given year by the gross domestic product (GDP) \u2014 defined as the sum of gross value added by all resident producers in the economy, including distributive trades and transport, plus any product taxes and minus any subsidies not included in the value of the products \u2014 and multiplying the result by 100.\n\nGross domestic expenditure on R&D (GERD) as a percentage of GDP (SDG Indicator 9.5.1) is the total intramural expenditure on R&D performed in the national territory during a specific reference period, expressed as a percentage of GDP of the national territory. Intramural R&D expenditures encompass all current expenditures plus gross fixed capital expenditures for R&D performed within a statistical unit during a specific reference period, regardless of the source of funds. The summation of intramural R&D expenditures across all sectors of the economy is equivalent to GERD.\n\n\n\n\n\n\n\nData are collected through national research and experimental development (R&D) surveys, either by the national statistical office or a line ministry (such as the Ministry for Science and Technology). The data compilers are the UNESCO Institute for Statistics (UIS), Organisation for Economic Co-operation and Development (OECD), Eurostat (Statistical Office of the European Union) and the Network on Science and Technology Indicators \u2013 Ibero-American and Inter-American (RICYT), African Science, Technology and Innovation (STI) Indicators Initiative (ASTII) of the African Union Development Agency-NEPAD (AUDA-NEPAD).\nStatistical concept(s): The gross domestic expenditure on R&D indicator consists of the total expenditure (current and capital) on R&D by all resident companies, research institutes, university and government laboratories, etc. It excludes R&D expenditures financed by domestic firms but performed abroad.\n\n\n\n\n\n\n\n\n\nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development.\n\n\n\n\n\n\n\n\n\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view\n\n\n\n\n\n\n\n\n\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n\n\n\n\n\n\n\n\n\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\n\n\n\n\n\n\n\n\n\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n\n\n\n\n1. Natural sciences;\n\n\n\n\n2. Engineering and technology;\n\n\n\n\n3. Medical and health sciences;\n\n\n\n\n4. Agricultural sciences;\n\n\n\n\n5. Social sciences;\n\n\n\n\n6. Humanities and the arts.\n\n\n\n\n\n\n\n\n\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors.\n\n### Development relevance:\nScience, technology, and innovation constitute pivotal elements for achieving sustainable growth. Sustainable Development Goal (SDG) target 9.5 is dedicated to the enhancement of scientific research and the advancement of technological capabilities within industrial sectors, with a particular focus on low- and middle-income countries. Furthermore, this target encompasses the objective of augmenting the cadre of research and development personnel, as well as escalating expenditures in research.\n\n### Limitations and exceptions:\nEstimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers.\n\n\n\n\n\n\n\nR&D totals from SNA accounts may differ from these estimates, due in part to the different treatments of software R&D in the totals.", "origins": [ { "producer": "UNESCO UIS Stat Bulk Data Download Service, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources/bulk, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-03-26, date published: 2025-02. Indicator GB.XPD.RSDV.GD.ZS (https://data.worldbank.org/indicator/GB.XPD.RSDV.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GB.XPD.RSDV.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "gc_ast_totl_cn": { "title": "Net acquisition of financial assets (current LCU)", "description_from_producer": "Net acquisition of government financial assets includes domestic and foreign financial claims, SDRs, and gold bullion held by monetary authorities as a reserve asset. The net acquisition of financial assets should be offset by the net incurrence of liabilities. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.AST.TOTL.CN (https://data.worldbank.org/indicator/GC.AST.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.AST.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_ast_totl_gd_zs": { "title": "Net acquisition of financial assets (% of GDP)", "description_from_producer": "Net acquisition of government financial assets includes domestic and foreign financial claims, SDRs, and gold bullion held by monetary authorities as a reserve asset. The net acquisition of financial assets should be offset by the net incurrence of liabilities. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.AST.TOTL.GD.ZS (https://data.worldbank.org/indicator/GC.AST.TOTL.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.AST.TOTL.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "gc_dod_totl_cn": { "title": "Central government debt, total (current LCU)", "description_from_producer": "Debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year. Central government is the part of general government that includes all administrative departments of the national executive, legislative, and judicial functions, other central agencies and those non-market producers controlled by the central government, whose competence extends normally over the whole economic territory. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the government debt. Statistics on government debt provide essential information for economic planning, as they can influence a country's fiscal policies and spending. Government debt levels are also a key indicator for investors, who use this information to assess the risk of investing in a country's bonds. High debt levels can lead to lower investor confidence and higher interest rates. Furthermore, debt statistics are crucial for evaluating the sustainability of a government's fiscal policy. They help determine whether adjustments are needed to avoid potential default. These statistics also enable international comparisons, allowing for benchmarking against other countries and identifying potential issues. Accurate debt statistics are vital for the formulation of monetary and fiscal policies, including decisions on taxation and government spending. They also promote public awareness by providing transparency and accountability in how public funds are managed. Lastly, credit rating agencies use government debt statistics to assign credit ratings, which affect a country's borrowing costs and its ability to attract investment. Overall, government debt statistics are a key component of a country's economic analysis and are essential for informed decision-making by policymakers, investors, and the public.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.DOD.TOTL.CN (https://data.worldbank.org/indicator/GC.DOD.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.DOD.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_dod_totl_gd_zs": { "title": "Central government debt, total (% of GDP)", "description_from_producer": "Debt is the entire stock of direct government fixed-term contractual obligations to others outstanding on a particular date. It includes domestic and foreign liabilities such as currency and money deposits, securities other than shares, and loans. It is the gross amount of government liabilities reduced by the amount of equity and financial derivatives held by the government. Because debt is a stock rather than a flow, it is measured as of a given date, usually the last day of the fiscal year. Central government is the part of general government that includes all administrative departments of the national executive, legislative, and judicial functions, other central agencies and those non-market producers controlled by the central government, whose competence extends normally over the whole economic territory. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the government debt. Statistics on government debt provide essential information for economic planning, as they can influence a country's fiscal policies and spending. Government debt levels are also a key indicator for investors, who use this information to assess the risk of investing in a country's bonds. High debt levels can lead to lower investor confidence and higher interest rates. Furthermore, debt statistics are crucial for evaluating the sustainability of a government's fiscal policy. They help determine whether adjustments are needed to avoid potential default. These statistics also enable international comparisons, allowing for benchmarking against other countries and identifying potential issues. Accurate debt statistics are vital for the formulation of monetary and fiscal policies, including decisions on taxation and government spending. They also promote public awareness by providing transparency and accountability in how public funds are managed. Lastly, credit rating agencies use government debt statistics to assign credit ratings, which affect a country's borrowing costs and its ability to attract investment. Overall, government debt statistics are a key component of a country's economic analysis and are essential for informed decision-making by policymakers, investors, and the public.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.DOD.TOTL.GD.ZS (https://data.worldbank.org/indicator/GC.DOD.TOTL.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.DOD.TOTL.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "gc_lbl_totl_cn": { "title": "Net incurrence of liabilities, total (current LCU)", "description_from_producer": "Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.LBL.TOTL.CN (https://data.worldbank.org/indicator/GC.LBL.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.LBL.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_lbl_totl_gd_zs": { "title": "Net incurrence of liabilities, total (% of GDP)", "description_from_producer": "Net incurrence of government liabilities includes foreign financing (obtained from nonresidents) and domestic financing (obtained from residents), or the means by which a government provides financial resources to cover a budget deficit or allocates financial resources arising from a budget surplus. The net incurrence of liabilities should be offset by the net acquisition of financial assets. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the transactions of the general government in financial assets. Data on transactions of the general government in financial assets provide information on how the government manages its investments and cash flow. These data help in assessing the government's fiscal strength, its ability to manage public debt, and the overall health of the economy. They are also used by policymakers to make informed decisions about monetary policy, budgeting, and economic planning. Additionally, these statistics are crucial for maintaining transparency and accountability in government operations, as they allow the public and investors to see how public funds are being utilized and managed. This can influence investor confidence and a country's credit rating, which in turn affects borrowing costs and investment levels. Overall, these data are essential for a comprehensive understanding of the government's financial position and for ensuring responsible financial governance.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.LBL.TOTL.GD.ZS (https://data.worldbank.org/indicator/GC.LBL.TOTL.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.LBL.TOTL.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "gc_nfn_totl_cn": { "title": "Net investment in nonfinancial assets (current LCU)", "description_from_producer": "Net investment in government nonfinancial assets includes fixed assets, inventories, valuables, and nonproduced assets. Nonfinancial assets are stores of value and provide benefits either through their use in the production of goods and services or in the form of property income and holding gains. Net investment in nonfinancial assets also includes consumption of fixed capital. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.NFN.TOTL.CN (https://data.worldbank.org/indicator/GC.NFN.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.NFN.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_nfn_totl_gd_zs": { "title": "Net investment in nonfinancial assets (% of GDP)", "description_from_producer": "Net investment in government nonfinancial assets includes fixed assets, inventories, valuables, and nonproduced assets. Nonfinancial assets are stores of value and provide benefits either through their use in the production of goods and services or in the form of property income and holding gains. Net investment in nonfinancial assets also includes consumption of fixed capital. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.NFN.TOTL.GD.ZS (https://data.worldbank.org/indicator/GC.NFN.TOTL.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.NFN.TOTL.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "gc_nld_totl_cn": { "title": "Net lending (+) / net borrowing (-) (current LCU)", "description_from_producer": "Net lending\u00a0(+) / net borrowing\u00a0(\u2013) equals government revenue minus expense, minus net investment in nonfinancial assets. It is also equal to the net result of transactions in financial assets and liabilities. Net lending/net borrowing is a summary measure indicating the extent to which government is either putting financial resources at the disposal of other sectors in the economy or abroad, or utilizing the financial resources generated by other sectors in the economy or from abroad. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is the balance of the government statistics. Balance items represent the difference between the total revenue and total expenditure of a government. If this balance is positive, it indicates net lending, meaning the government has a surplus and can potentially lend or invest the excess funds. Conversely, if the balance is negative, it indicates net borrowing, meaning the government has a deficit and may need to borrow funds to cover the shortfall. This figure is important because it provides a clear indicator of a government's fiscal health and its ability to finance its operations without resorting to additional borrowing. It is also a key indicator used by policymakers to make informed decisions about fiscal policy, taxation, and public spending. Furthermore, it is an essential metric for international organizations, investors, and credit rating agencies to assess a country's economic stability and creditworthiness.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.NLD.TOTL.CN (https://data.worldbank.org/indicator/GC.NLD.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.NLD.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_nld_totl_gd_zs": { "title": "Net lending (+) / net borrowing (-) (% of GDP)", "description_from_producer": "Net lending\u00a0(+) / net borrowing\u00a0(\u2013) equals government revenue minus expense, minus net investment in nonfinancial assets. It is also equal to the net result of transactions in financial assets and liabilities. Net lending/net borrowing is a summary measure indicating the extent to which government is either putting financial resources at the disposal of other sectors in the economy or abroad, or utilizing the financial resources generated by other sectors in the economy or from abroad. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is the balance of the government statistics. Balance items represent the difference between the total revenue and total expenditure of a government. If this balance is positive, it indicates net lending, meaning the government has a surplus and can potentially lend or invest the excess funds. Conversely, if the balance is negative, it indicates net borrowing, meaning the government has a deficit and may need to borrow funds to cover the shortfall. This figure is important because it provides a clear indicator of a government's fiscal health and its ability to finance its operations without resorting to additional borrowing. It is also a key indicator used by policymakers to make informed decisions about fiscal policy, taxation, and public spending. Furthermore, it is an essential metric for international organizations, investors, and credit rating agencies to assess a country's economic stability and creditworthiness.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.NLD.TOTL.GD.ZS (https://data.worldbank.org/indicator/GC.NLD.TOTL.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.NLD.TOTL.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "gc_rev_gotr_cn": { "title": "Grants and other revenue (current LCU)", "description_from_producer": "Grants are transfers receivable by government units, from other resident or nonresident government units or international organizations, that do not meet the definition of a tax, subsidy, or social contribution. Other revenue is all revenue receivable excluding taxes, social contributions, and grants. This category of revenue includes property income, sales of goods and services, and miscellaneous other types of revenue. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.REV.GOTR.CN (https://data.worldbank.org/indicator/GC.REV.GOTR.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.REV.GOTR.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_rev_gotr_zs": { "title": "Grants and other revenue (% of revenue)", "description_from_producer": "Grants are transfers receivable by government units, from other resident or nonresident government units or international organizations, that do not meet the definition of a tax, subsidy, or social contribution. Other revenue is all revenue receivable excluding taxes, social contributions, and grants. This category of revenue includes property income, sales of goods and services, and miscellaneous other types of revenue. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.REV.GOTR.ZS (https://data.worldbank.org/indicator/GC.REV.GOTR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.REV.GOTR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of revenue", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of revenue" } }, "gc_rev_socl_cn": { "title": "Social contributions (current LCU)", "description_from_producer": "Social contributions are actual or imputed contributions payable to social insurance schemes to\n\n\n\n\nmake provisions for social benefits to be paid. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.REV.SOCL.CN (https://data.worldbank.org/indicator/GC.REV.SOCL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.REV.SOCL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_rev_socl_zs": { "title": "Social contributions (% of revenue)", "description_from_producer": "Social contributions are actual or imputed contributions payable to social insurance schemes to\n\n\n\n\nmake provisions for social benefits to be paid. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.REV.SOCL.ZS (https://data.worldbank.org/indicator/GC.REV.SOCL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.REV.SOCL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of revenue", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of revenue" } }, "gc_rev_xgrt_cn": { "title": "Revenue, excluding grants (current LCU)", "description_from_producer": "Revenue is an increase in net worth resulting from a transaction. Grants are excluded from this figure. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.REV.XGRT.CN (https://data.worldbank.org/indicator/GC.REV.XGRT.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.REV.XGRT.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_rev_xgrt_gd_zs": { "title": "Revenue, excluding grants (% of GDP)", "description_from_producer": "Revenue is an increase in net worth resulting from a transaction. Grants are excluded from this figure. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.REV.XGRT.GD.ZS (https://data.worldbank.org/indicator/GC.REV.XGRT.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.REV.XGRT.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "gc_tax_expt_cn": { "title": "Taxes on exports (current LCU)", "description_from_producer": "Export taxes are taxes on goods or services that become payable to government when the goods leave the economic territory or when the services are delivered to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.EXPT.CN (https://data.worldbank.org/indicator/GC.TAX.EXPT.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.EXPT.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_tax_expt_zs": { "title": "Taxes on exports (% of tax revenue)", "description_from_producer": "Export taxes are taxes on goods or services that become payable to government when the goods leave the economic territory or when the services are delivered to non-residents. This indicator is expressed as a percentage of tax revenue which includes compulsory, unrequited payments, in cash or in kind, made by institutional units to government units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.EXPT.ZS (https://data.worldbank.org/indicator/GC.TAX.EXPT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.EXPT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of tax revenue", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of tax revenue" } }, "gc_tax_gsrv_cn": { "title": "Taxes on goods and services (current LCU)", "description_from_producer": "General taxes on goods and services are taxes levied on the production, leasing, delivery, sale, purchase or other change of ownership of a wide range of goods and the provision of a wide range of services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.GSRV.CN (https://data.worldbank.org/indicator/GC.TAX.GSRV.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.GSRV.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_tax_gsrv_rv_zs": { "title": "Taxes on goods and services (% of revenue)", "description_from_producer": "General taxes on goods and services are taxes levied on the production, leasing, delivery, sale, purchase or other change of ownership of a wide range of goods and the provision of a wide range of services. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.GSRV.RV.ZS (https://data.worldbank.org/indicator/GC.TAX.GSRV.RV.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.GSRV.RV.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of revenue", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of revenue" } }, "gc_tax_gsrv_va_zs": { "title": "Taxes on goods and services (% value added of industry and services)", "description_from_producer": "General taxes on goods and services are taxes levied on the production, leasing, delivery, sale, purchase or other change of ownership of a wide range of goods and the provision of a wide range of services. This indicator is expressed as a percentage of value added in industry and services which is the contribution to the economy by industries in ISIC (Rev. 3) divisions 05-43 and 50-99.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.GSRV.VA.ZS (https://data.worldbank.org/indicator/GC.TAX.GSRV.VA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.GSRV.VA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% value added of industry and services", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% value added of industry and services" } }, "gc_tax_impt_cn": { "title": "Customs and other import duties (current LCU)", "description_from_producer": "Taxes and duties on imports are taxes on goods and services that become payable at the moment when goods enter the economic territory or when services are delivered by non-resident producers to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.IMPT.CN (https://data.worldbank.org/indicator/GC.TAX.IMPT.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.IMPT.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_tax_impt_zs": { "title": "Customs and other import duties (% of tax revenue)", "description_from_producer": "Taxes and duties on imports are taxes on goods and services that become payable at the moment when goods enter the economic territory or when services are delivered by non-resident producers to residents. This indicator is expressed as a percentage of tax revenue which includes compulsory, unrequited payments, in cash or in kind, made by institutional units to government units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.IMPT.ZS (https://data.worldbank.org/indicator/GC.TAX.IMPT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.IMPT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of tax revenue", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of tax revenue" } }, "gc_tax_intt_cn": { "title": "Taxes on international trade (current LCU)", "description_from_producer": "Taxes on international trade are taxes that become payable when goods cross the national or customs frontiers of the economic territory or when transactions in services exchange between residents and non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.INTT.CN (https://data.worldbank.org/indicator/GC.TAX.INTT.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.INTT.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_tax_intt_rv_zs": { "title": "Taxes on international trade (% of revenue)", "description_from_producer": "Taxes on international trade are taxes that become payable when goods cross the national or customs frontiers of the economic territory or when transactions in services exchange between residents and non-residents. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.INTT.RV.ZS (https://data.worldbank.org/indicator/GC.TAX.INTT.RV.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.INTT.RV.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of revenue", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of revenue" } }, "gc_tax_othr_cn": { "title": "Other taxes (current LCU)", "description_from_producer": "Other taxes include employer payroll or labor taxes, taxes on property, and taxes not allocable to other categories, such as penalties for late payment or nonpayment of taxes. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.OTHR.CN (https://data.worldbank.org/indicator/GC.TAX.OTHR.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.OTHR.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_tax_othr_rv_zs": { "title": "Other taxes (% of revenue)", "description_from_producer": "Other taxes include employer payroll or labor taxes, taxes on property, and taxes not allocable to other categories, such as penalties for late payment or nonpayment of taxes. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.OTHR.RV.ZS (https://data.worldbank.org/indicator/GC.TAX.OTHR.RV.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.OTHR.RV.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of revenue", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of revenue" } }, "gc_tax_totl_cn": { "title": "Tax revenue (current LCU)", "description_from_producer": "Taxes are compulsory, unrequited payments, in cash or in kind, made by institutional\n\n\n\n\nunits to government units. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.TOTL.CN (https://data.worldbank.org/indicator/GC.TAX.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_tax_totl_gd_zs": { "title": "Tax revenue (% of GDP)", "description_from_producer": "Taxes are compulsory, unrequited payments, in cash or in kind, made by institutional\n\n\n\n\nunits to government units. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.TOTL.GD.ZS (https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "gc_tax_ypkg_cn": { "title": "Taxes on income, profits and capital gains (current LCU)", "description_from_producer": "Taxes on income, profits, and capital gains are taxes payable on the actual or presumed incomes, profits and capital gains. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.YPKG.CN (https://data.worldbank.org/indicator/GC.TAX.YPKG.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.YPKG.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_tax_ypkg_rv_zs": { "title": "Taxes on income, profits and capital gains (% of revenue)", "description_from_producer": "Taxes on income, profits, and capital gains are taxes payable on the actual or presumed incomes, profits and capital gains. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.YPKG.RV.ZS (https://data.worldbank.org/indicator/GC.TAX.YPKG.RV.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.YPKG.RV.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of revenue", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of revenue" } }, "gc_tax_ypkg_zs": { "title": "Taxes on income, profits and capital gains (% of total taxes)", "description_from_producer": "Taxes on income, profits, and capital gains are taxes payable on the actual or presumed incomes, profits and capital gains. This indicator is expressed as a percentage of total taxes which includes all compulsory, unrequited payments, in cash or in kind, made by institutional units to government units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the revenue side of government finance statistics. The revenue side of government finance statistics provides insight into the government's income streams, which include taxes, fees, fines, and revenues from state-owned entities. This data is crucial for evaluating the government's financial capabilities to fund public services and infrastructure projects. It also helps determining the sustainability and effectiveness of fiscal policies. By examining revenue trends, policymakers can make informed decisions on tax reforms, budget allocations, and debt management. For the public and investors, understanding government revenue is important for gauging the country's economic health and the government's ability to honor its financial commitments, which can affect economic stability and confidence in the market.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.TAX.YPKG.ZS (https://data.worldbank.org/indicator/GC.TAX.YPKG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.TAX.YPKG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total taxes", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total taxes" } }, "gc_xpn_comp_cn": { "title": "Compensation of employees (current LCU)", "description_from_producer": "Compensation of employees is defined as the total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.COMP.CN (https://data.worldbank.org/indicator/GC.XPN.COMP.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.COMP.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_xpn_comp_zs": { "title": "Compensation of employees (% of expense)", "description_from_producer": "Compensation of employees is defined as the total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.COMP.ZS (https://data.worldbank.org/indicator/GC.XPN.COMP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.COMP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of expense", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of expense" } }, "gc_xpn_gsrv_cn": { "title": "Goods and services expense (current LCU)", "description_from_producer": "Goods and services include all government payments in exchange for goods and services used for the production of market and nonmarket goods and services. Use of goods and services for account capital formation is excluded. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.GSRV.CN (https://data.worldbank.org/indicator/GC.XPN.GSRV.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.GSRV.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_xpn_gsrv_zs": { "title": "Goods and services expense (% of expense)", "description_from_producer": "Goods and services include all government payments in exchange for goods and services used for the production of market and nonmarket goods and services. Use of goods and services for own account capital formation is excluded. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.GSRV.ZS (https://data.worldbank.org/indicator/GC.XPN.GSRV.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.GSRV.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of expense", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of expense" } }, "gc_xpn_intp_cn": { "title": "Interest payments (current LCU)", "description_from_producer": "Interest payments include interest payments on government debt (including long-term bonds, long-term loans, and other debt instruments) to domestic and foreign residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.INTP.CN (https://data.worldbank.org/indicator/GC.XPN.INTP.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.INTP.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_xpn_intp_rv_zs": { "title": "Interest payments (% of revenue)", "description_from_producer": "Interest payments include interest payments on government debt (including long-term bonds, long-term loans, and other debt instruments) to domestic and foreign residents. This indicator is expressed as a percentage of revenue which includes all transactions that add to the amount of economic value of a unit or sector.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.INTP.RV.ZS (https://data.worldbank.org/indicator/GC.XPN.INTP.RV.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.INTP.RV.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of revenue", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of revenue" } }, "gc_xpn_intp_zs": { "title": "Interest payments (% of expense)", "description_from_producer": "Interest payments include interest payments on government debt (including long-term bonds, long-term loans, and other debt instruments) to domestic and foreign residents. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.INTP.ZS (https://data.worldbank.org/indicator/GC.XPN.INTP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.INTP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of expense", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of expense" } }, "gc_xpn_othr_cn": { "title": "Other expense (current LCU)", "description_from_producer": "Other expense is spending on dividends, rent, and other miscellaneous expenses, including provision for consumption of fixed capital. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.OTHR.CN (https://data.worldbank.org/indicator/GC.XPN.OTHR.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.OTHR.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_xpn_othr_zs": { "title": "Other expense (% of expense)", "description_from_producer": "Other expense is spending on dividends, rent, and other miscellaneous expenses, including provision for consumption of fixed capital. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.OTHR.ZS (https://data.worldbank.org/indicator/GC.XPN.OTHR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.OTHR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of expense", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of expense" } }, "gc_xpn_totl_cn": { "title": "Expense (current LCU)", "description_from_producer": "Expense is a decrease in net worth resulting from a transaction. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.TOTL.CN (https://data.worldbank.org/indicator/GC.XPN.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_xpn_totl_gd_zs": { "title": "Expense (% of GDP)", "description_from_producer": "Expense is a decrease in net worth resulting from a transaction. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF), OECD, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF);\nWorld Development Indicators, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD). Indicator GC.XPN.TOTL.GD.ZS (https://data.worldbank.org/indicator/GC.XPN.TOTL.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.TOTL.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "gc_xpn_trft_cn": { "title": "Subsidies and other transfers (current LCU)", "description_from_producer": "Subsidies are current unrequited payments that government units, including nonresident government units, make to enterprises on the basis of the levels of their production activities or the quantities or values of the goods or services that they produce, sell, export or import. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.TRFT.CN (https://data.worldbank.org/indicator/GC.XPN.TRFT.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.TRFT.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "gc_xpn_trft_zs": { "title": "Subsidies and other transfers (% of expense)", "description_from_producer": "Subsidies are current unrequited payments that government units, including nonresident government units, make to enterprises on the basis of the levels of their production activities or the quantities or values of the goods or services that they produce, sell, export or import. This indicator is expressed as percentage of total expenses which is any decrease in net worth resulting from a transaction.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government Finance statistics are compiled in accordance with international standards: Government Finance Statistics Manual, 2014 or 2001 editions. Specific information on how countries compile their Government Finance statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): Government Financial Statistics are compiled within a conceptual and reporting framework suitable for analyzing and evaluating fiscal policy, especially the performance of the general government sector and the broader public sector of any economy.\n\n### Development relevance:\nThis indicator is related to Government finance statistics. Government finance statistics provide a detailed snapshot of a government's fiscal operations and health. They encompass data on revenue, expenditures, deficits, and debt levels, which are essential for crafting fiscal policy, budget planning, and economic forecasting. These statistics help policymakers manage public finances effectively, make informed decisions on taxation and spending, and set priorities for resource allocation. For investors, analysts, and the international community, government finance statistics serve as key indicators of a country's economic stability and creditworthiness. They also play a crucial role in ensuring transparency and accountability, as they allow citizens and oversight bodies to track how public funds are managed and spent, fostering democratic engagement and governance. More specifically, this indicator is related to the expense side of government finance statistics. The expense side of government finance statistics provides helpful detailed information on how a government spends its funds, including expenditures on public services, social programs, infrastructure, and debt servicing. This data is critical for assessing the effectiveness and efficiency of government spending and for ensuring that resources are being allocated to priority areas that support economic and social development. It also helps in evaluating the fiscal sustainability of government operations, as persistent high levels of spending relative to revenue can lead to budget deficits and increasing public debt. For citizens, understanding how their government is spending money is key to holding it accountable and ensuring that public funds are used in the public interest. For investors and credit rating agencies, the expense data is important for assessing a country's fiscal health and the risks associated with its sovereign debt.\n\n### Limitations and exceptions:\nFor most countries central government finance data have been consolidated into one account, but for others only budgetary central government accounts are available. Countries reporting budgetary data are noted in the country metadata. Because budgetary accounts may not include all central government units (such as social security funds), they usually provide an incomplete picture. In federal states the central government accounts provide an incomplete view of total public finance.\n\n\n\n\n\n\n\n\n\nData on government revenue and expense are collected by the IMF through questionnaires to member countries and by the Organisation for Economic Co-operation and Development (OECD). Despite IMF efforts to standardize data collection, statistics are often incomplete, untimely, and not comparable across countries.", "origins": [ { "producer": "International Monetary Fund (IMF) Government Finance Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Government Finance Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator GC.XPN.TRFT.ZS (https://data.worldbank.org/indicator/GC.XPN.TRFT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GC.XPN.TRFT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of expense", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of expense" } }, "gd_wbl_ovl_enf": { "title": "WBL: Enforcement Perceptions Index (scale 0-100)", "description_from_producer": "The index is a composite measure (0\u2013100) that summarizes how enforcement perceptions shape women\u2019s economic opportunities across ten topics: Safety, Mobility, Work, Pay, Marriage, Parenthood, Childcare, Entrepreneurship, Assets, and Pension. It is calculated as the unweighted average of the ten topic scores, with 100 representing the highest possible score and 0 the lowest. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf).\n\n### Aggregation method:\nSimple average\n\n### Statistical concept and methodology:\nMethodology: Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication.\n\n### Development relevance:\nWomen, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women\u2019s economic participation. Laws are fundamental to women\u2019s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women\u2019s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth.\n\n### Limitations and exceptions:\nWBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women\u2019s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets.\n\n### Other notes:\n1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group.", "origins": [ { "producer": "Women, Business and the Law, World Bank (WB), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Women, Business and the Law, World Bank (WB), uri: https://wbl.worldbank.org/en/wbl-data, date accessed: 2026-03-24, date published: 2026-02-24. Indicator GD_WBL_OVL_ENF (https://data.worldbank.org/indicator/GD_WBL_OVL_ENF). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GD_WBL_OVL_ENF", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gd_wbl_ovl_law": { "title": "WBL: Legal Framework Index (scale 0-100)", "description_from_producer": "The index refers to the composite measure (0\u2013100) that summarizes how legal frameworks shape women\u2019s economic opportunities across ten topics: Safety, Mobility, Work, Pay, Marriage, Parenthood, Childcare, Entrepreneurship, Assets, and Pension. It is calculated by taking the unweighted average of the ten topic scores, with 100 representing the highest possible score and 0 the lowest. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)\n\n### Aggregation method:\nSimple average\n\n### Statistical concept and methodology:\nMethodology: Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication.\n\n### Development relevance:\nWomen, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women\u2019s economic participation. Laws are fundamental to women\u2019s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women\u2019s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth.\n\n### Limitations and exceptions:\nWBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women\u2019s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets.\n\n### Other notes:\n1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group.", "origins": [ { "producer": "Women, Business and the Law, World Bank (WB), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Women, Business and the Law, World Bank (WB), uri: https://wbl.worldbank.org/en/wbl-data, date accessed: 2026-03-24, date published: 2026-02-24. Indicator GD_WBL_OVL_LAW (https://data.worldbank.org/indicator/GD_WBL_OVL_LAW). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GD_WBL_OVL_LAW", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gd_wbl_ovl_sfr": { "title": "WBL: Supportive Framework Index (scale 0-100)", "description_from_producer": "The index refers to the composite measure (0\u2013100) that summarizes how supporting frameworks shape women\u2019s economic opportunities across ten topics: Safety, Mobility, Work, Pay, Marriage, Parenthood, Childcare, Entrepreneurship, Assets, and Pension. It is calculated by taking the unweighted average of the ten topic scores, with 100 representing the highest possible score and 0 the lowest. For details, please refer to the WBL Methodology Handbook (link: https://wbl.worldbank.org/content/dam/sites/wbl/documents/2026/WBL-Methodology-Handbook-2026-FINAL.pdf)\n\n### Aggregation method:\nSimple average\n\n### Statistical concept and methodology:\nMethodology: Women, Business and the Law tracks progress on the enabling environment for women's economic opportunity in 190 economies. Data are collected with standardized questionnaires to ensure comparability across economies. Questionnaires are administered to over 20,000 respondents with expertise in family, labor, and violence against women legislation, including lawyers, judges, academics, and members of civil society organizations working on gender issues. Respondents provide responses to the questionnaires and references to relevant laws and policies. All submissions are validated against codified laws, official government sources, and standardized protocols to ensure accuracy, comparability, and transparency. The Women, Business and the Law data reflect laws and policies in force through October 1 of the year prior to publication.\n\n### Development relevance:\nWomen, Business and the Law (WBL) provides comprehensive and comparable data on the legal, supportive, and enforcement environments shaping women\u2019s economic participation. Laws are fundamental to women\u2019s economic opportunities because they determine whether women can work, start and run businesses, own and inherit assets, access finance, and receive equal pay and protections. Legal barriers can directly restrict women\u2019s participation in the labor market and entrepreneurship, while enabling laws can expand access to jobs, income, and economic security. By expanding beyond laws on the books to include supportive frameworks and expert assessments of enforcement, WBL helps identify gaps between formal legal reforms and their implementation in practice. The data support cross-country benchmarking, evidence-based policy dialogue, and research on the relationship between gender equality, productivity, and economic growth.\n\n### Limitations and exceptions:\nWBL measures formal legal frameworks, supportive policies, and expert perceptions of enforcement, but it does not capture all social norms, informal practices, subnational variation, or intersectional inequalities that may influence women\u2019s economic outcomes. Enforcement indicators rely on expert assessments, which may not fully reflect actual experiences on the ground. The dataset focuses on codified national law and operational government policies; uncodified customary or personal laws are excluded unless codified or explicitly referenced and established through judicial decisions. General parameters (e.g., main business city, adult lawful citizen) are used to ensure cross-economy comparability. In addition, reporting years in WBL may differ from reference years used in other databases such as WDI or the Gender Data Portal, which can lead to timing differences when integrating datasets.\n\n### Other notes:\n1. Reference period: In the WDI and Gender Databases, the reference period reflects the data coverage year, not the WBL report year shown on the WBL website. For example, WBL 2026 (report year) corresponds to 2025data in WDI and the Gender Database.\n\n2. Methodology: The 2026 Women, Business and the Law (WBL) report uses the revised WBL 2.0 methodology. The WBL series in the Gender Database reflects WBL 2.0 as of March 2, 2026, and the WBL series in the World Development Indicators (WDI) reflects WBL 2.0 as of April 2026. Further details are available in the WBL Methodology Handbook (2026). Data based on the previous methodology (WBL 1.0), including the historical series, remain available on the WBL data download page for reference.\n\n3. Income group classification: Classifications follow the World Bank Country and Lending Groups for fiscal year 2026. Ethiopia and Venezuela, RB are not assigned to an income group.", "origins": [ { "producer": "Women, Business and the Law, World Bank (WB), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Women, Business and the Law, World Bank (WB), uri: https://wbl.worldbank.org/en/wbl-data, date accessed: 2026-03-24, date published: 2026-02-24. Indicator GD_WBL_OVL_SFR (https://data.worldbank.org/indicator/GD_WBL_OVL_SFR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GD_WBL_OVL_SFR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gf_xpd_budg_zs": { "title": "Primary government expenditures as a proportion of original approved budget (%)", "description_from_producer": "Primary government expenditures as a proportion of original approved budget measures the extent to which aggregate budget expenditure outturn reflects the amount originally approved, as defined in government budget documentation and fiscal reports. The coverage is budgetary central government (BCG) and the time period covered is the last three completed fiscal years.\n\n### Statistical concept and methodology:\nMethodology: The PEFA PI-1 Indicator is used as a basis for the SDG 16.6.1 Indicator, following the measurement guidance and coverage. In order to make the computation and the analysis of data over time easy and applicable for all countries, it was decided that SDG 16.6.1 indicator will be based on the annual data collection on approved and executed budgets for all countries and will be calculated annually.\n\nThe simple calculation for every year for every country is for the: Aggregate expenditure outturn = Executed Budget/Approved Budget*100\n\nIn the countries and regional groupings, analysis of the deviations are done according regions/years/countries, using the requirements of PEFA PI-1 indicator.\n\nAlthough the computation and scoring used for PI-1 indicator are not applied for the SDG 16.6.1 indicator, the categorization described below is applied and is the basis for the SDG 16.6.1 indicator. PEFA Methodology The methodology for calculating the PEFA PI-1 indicator is provided in a spreadsheet (titled \u201cEn PI-1 and PI-2 Exp Calculation-Feb 1 2016 (xls)\u201d) and is based on the PEFA Public Expenditure and Financial Accountability (PEFA) Framework.\n\nScoring is at the heart of the indicator. A country is scored separately on a four-point ordinal scale: A, B, C, or D, according to precise criteria: (A) Aggregate expenditure outturn was between 95% and 105% of the approved aggregate budgeted expenditure in at least two of the last three years. (B) Aggregate expenditure outturn was between 90% and 110% of the approved aggregate budgeted expenditure in at least two of the last three years. (C Aggregate expenditure outturn was between 85% and 115% of the approved aggregate budgeted expenditure in at least two of the last three years. (D) Performance is less than required for a C score. In order to justify a particular score, every aspect specified in the scoring requirements must be fulfilled.\n\nIf the requirements are only partly met, the criteria are not satisfied and a lower score should be given that coincides with achievement of all requirements for the lower performance rating. A score of C reflects the basic level of performance for each indicator and dimension, consistent with good international practices. A score of D means that the feature being measured is present at less than the basic level of performance or is absent altogether, or that there is insufficient information to score the dimension.\n\nThe D score indicates performance that falls below the basic level. \u2018D\u2019 is applied if the performance observed is less than required for any higher score. For this reason, a D score is warranted when sufficient information is not available to establish the actual level of performance. A score of D due to insufficient information is distinguished from D scores for low-level performance by the use of an asterisk\u2014that is, D* at the dimension level. The asterisk is not included at the indicator level.\n\nThe coverage is budgetary central government (BCG) and requires data for three consecutive years as a basis for assessment. The data would cover the most recent completed fiscal year for which data is available and the two immediately preceding years.\nStatistical concept(s): This indicator measures the extent to which aggregate budget expenditure outturn reflects the amount originally approved, as defined in government budget documentation and fiscal reports. The coverage is budgetary central government and the time period covers every fiscal year for the countries.\n\nRefer to Other notes for more details.\n\n### Development relevance:\nThe indicator attempts to capture the reliability of government budgets: do governments spend what they intend to and do they collect what they set out to collect. The ability to implement the enacted budget is an important factor in government\u2019s ability to deliver public services and achieve development objectives. The deviation between approved and actual spending is measured over a 12-month period (the budget year) and may have important implications for macroeconomic stability, public service delivery, and social welfare. A credibly implemented budget has only small deviations from the approved one. If expenditure is under-executed, beneficiaries may not receive crucial services. Over-executed budgets may result in budget deficits and increased public debt levels and can influence the macroeconomic stability. In both cases, lack of budget credibility undermines the usefulness of the budget process for policy making and implementation and erodes public trust in government.\n\n### Limitations and exceptions:\nAlthough not all countries have used the PEFA methodology on an annual basis for the PEFA PI-1 indicator, the methodology relies on standard data sets for approved and final budget outturns which are commonly produced at least annually in every country. The countries that have not used the methodology to date are primarily highly developed countries which would have less difficulty in providing the necessary data than those in the lower and middle income categories that have been primary users of Public Expenditure and Financial Accountability (PEFA) to date. (As of: 2024-07-29)\n\nOne limitation of the indicator is that it is an aggregate indicator of budget reliability. While it can be disaggregated across regions, it is not disaggregated across various budget subcomponents. Different indicators are used for assessing changes in expenditure composition in the PEFA framework. Also, while this indicator is intended to measure budget reliability it should be understood that actual expenditure outturns can deviate from the originally approved budget for reasons unrelated to the accuracy of forecasts\u2014for example, as a result of a major macroeconomic shock. However, the calibration of this indicator accommodates one unusual or \u201coutlier\u201d year and focuses on deviations from the forecast which occur in two of the three years covered by the assessment. Therefore, single year shocks are discounted allowing a more balanced assessment.\n\nThe broader context in which the indicator was developed is as follows. PEFA is a tool for assessing the status of public financial management and reporting on the strengths and weaknesses of Public Financial Management (PFM). A PEFA assessment provides a thorough, consistent and evidence-based analysis of PFM performance at a specific point in time and can be reapplied in successive assessments to track changes over time. The PEFA framework provides the foundation for evidence-based measurement of countries\u2019 PFM systems using 31 performance indicators that are further disaggregated into 94 dimensions. A PEFA assessment measures the extent to which PFM systems, processes and institutions contribute to the achievement of desirable budget outcomes: aggregate fiscal discipline, strategic allocation of resources, and efficient service delivery.\n\n### Other notes:\nAggregate expenditure includes actual expenditures incorporating those incurred as a result of unplanned or exceptional events\u2014for example, armed conflicts or natural disasters. Expenditures financed by windfall revenues, including privatization, should be included and noted in the supporting fiscal tables and narrative. Expenditures financed externally by loans or grants should be included, if covered by the budget along with contingency vote(s) and interest on debt.\n\nExpenditure assigned to suspense accounts is not included in the aggregate. However, if amounts are held in suspense accounts at the end of any year that could affect the scores if included in the calculations, they can be included. In such cases the reason(s) for inclusion must be clearly stated.\n\nActual expenditure outturns can deviate from the originally approved budget for reasons unrelated to the accuracy of forecasts\u2014for example, as a result of a major macroeconomic shock. The calibration of this indicator accommodates one unusual or \u201coutlier\u201d year and focuses on deviations from the forecast which occur in two of the three years covered by the assessment.\nDetailed resources are available at www.pefa.org", "origins": [ { "producer": "Public Expenditure and Financial Accountability (PEFA), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Public Expenditure and Financial Accountability (PEFA), World Bank (WB), uri: https://www.pefa.org/node/5239, note: The raw data collected in order to calculate this indicator are the initially Approved and Executed Budgets. Budget Laws of countries is the usual source of the approved budget of countries. The end-of year fiscal reports (budget execution reports) are the sources of the actual spending. This data is typically obtained from websites of the Ministry of Finance (MoF) or the national Parliament, or data are collected through communication with the MoF. Indicator GF.XPD.BUDG.ZS (https://data.worldbank.org/indicator/GF.XPD.BUDG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GF.XPD.BUDG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_cc_est": { "title": "Control of Corruption - Governance estimate (approx. -2.5 to +2.5)", "description_from_producer": "Control of Corruption (CC) captures perceptions of the extent to which public power is used for private gain, including both petty and grand corruption, as well as capture of the state by elites and private interests.\n\nGovernance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) standard error. The number of sources indicates the number of underlying data sources on which the governance estimate is based. The standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_CC_EST (https://data.worldbank.org/indicator/GOV_WGI_CC_EST). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_CC_EST", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_cc_sc": { "title": "Control of Corruption - Governance score (0-100)", "description_from_producer": "Control of Corruption (CC) captures perceptions of the extent to which public power is used for private gain, including both petty and grand corruption, as well as capture of the state by elites and private interests.\n\nGovernance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) confidence intervals (lower and upper bound). The number of sources indicates the number of underlying data sources on which the governance score (respectively the underlying governance estimate) is based. The confidence intervals include the lower and upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated).\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_CC_SC (https://data.worldbank.org/indicator/GOV_WGI_CC_SC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_CC_SC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_ge_est": { "title": "Government Effectiveness - Governance estimate (approx. -2.5 to +2.5)", "description_from_producer": "Government Effectiveness (GE) captures perceptions of the quality of public services, the civil service, policy formulation and implementation, and the credibility of a government\u2019s decisions.\n\nGovernance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) standard error. The number of sources indicates the number of underlying data sources on which the governance estimate is based. The standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_GE_EST (https://data.worldbank.org/indicator/GOV_WGI_GE_EST). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_GE_EST", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_ge_sc": { "title": "Government Effectiveness - Governance score (0-100)", "description_from_producer": "Government Effectiveness (GE) captures perceptions of the quality of public services, the civil service, policy formulation and implementation, and the credibility of a government\u2019s decisions.\n\nGovernance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) confidence intervals (lower and upper bound). The number of sources indicates the number of underlying data sources on which the governance score (respectively the underlying governance estimate) is based. The confidence intervals include the lower and upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated).\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_GE_SC (https://data.worldbank.org/indicator/GOV_WGI_GE_SC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_GE_SC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_pv_est": { "title": "Political Stability - Governance estimate (approx. -2.5 to +2.5)", "description_from_producer": "Political Stability (PV) captures perceptions of the extent to which political power and governance are secure from destabilization, and of the likelihood that authority will be challenged or altered through violent, coercive, or unconstitutional means.\n\nGovernance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) standard error. The number of sources indicates the number of underlying data sources on which the governance estimate is based. The standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_PV_EST (https://data.worldbank.org/indicator/GOV_WGI_PV_EST). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_PV_EST", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_pv_sc": { "title": "Political Stability - Governance score (0-100)", "description_from_producer": "Political Stability (PV) captures perceptions of the extent to which political power and governance are secure from destabilization, and of the likelihood that authority will be challenged or altered through violent, coercive, or unconstitutional means.\n\nGovernance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) confidence intervals (lower and upper bound). The number of sources indicates the number of underlying data sources on which the governance score (respectively the underlying governance estimate) is based. The confidence intervals include the lower and upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated).\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_PV_SC (https://data.worldbank.org/indicator/GOV_WGI_PV_SC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_PV_SC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_rl_est": { "title": "Rule of Law - Governance estimate (approx. -2.5 to +2.5)", "description_from_producer": "Rule of Law (RL) captures perceptions of the extent to which agents respect and follow the rules of society, including contract enforcement, property rights, the police, courts, and the likelihood of crime and violence.\n\nGovernance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) standard error. The number of sources indicates the number of underlying data sources on which the governance estimate is based. The standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_RL_EST (https://data.worldbank.org/indicator/GOV_WGI_RL_EST). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_RL_EST", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_rl_sc": { "title": "Rule of Law - Governance score (0-100)", "description_from_producer": "Rule of Law (RL) captures perceptions of the extent to which agents respect and follow the rules of society, including contract enforcement, property rights, the police, courts, and the likelihood of crime and violence.\n\nGovernance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) confidence intervals (lower and upper bound). The number of sources indicates the number of underlying data sources on which the governance score (respectively the underlying governance estimate) is based. The confidence intervals include the lower and upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated).\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_RL_SC (https://data.worldbank.org/indicator/GOV_WGI_RL_SC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_RL_SC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_rq_est": { "title": "Regulatory Quality - Governance estimate (approx. -2.5 to +2.5)", "description_from_producer": "Regulatory Quality (RQ) captures perceptions of the government\u2019s ability to design and implement policies and regulations that promote private sector development.\n\nGovernance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) standard error. The number of sources indicates the number of underlying data sources on which the governance estimate is based. The standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_RQ_EST (https://data.worldbank.org/indicator/GOV_WGI_RQ_EST). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_RQ_EST", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_rq_sc": { "title": "Regulatory Quality - Governance score (0-100)", "description_from_producer": "Regulatory Quality (RQ) captures perceptions of the government\u2019s ability to design and implement policies and regulations that promote private sector development.\n\nGovernance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) confidence intervals (lower and upper bound). The number of sources indicates the number of underlying data sources on which the governance score (respectively the underlying governance estimate) is based. The confidence intervals include the lower and upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated).\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_RQ_SC (https://data.worldbank.org/indicator/GOV_WGI_RQ_SC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_RQ_SC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_va_est": { "title": "Voice and Accountability - Governance estimate (approx. -2.5 to +2.5)", "description_from_producer": "Voice and Accountability (VA) captures perceptions of the extent to which citizens can participate in selecting their government including electoral integrity, and of accountability mechanisms for citizens\u2014reflected in the ability to access information, governmental oversight bodies, and a robust traditional/digital media landscape.\n\nGovernance estimate from the aggregation model, in units of a standard normal distribution, i.e. ranging from approximately -2.5 to 2.5. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) standard error. The number of sources indicates the number of underlying data sources on which the governance estimate is based. The standard error indicates the precision of the governance estimate. Larger values indicate less precise estimates. A 90% confidence interval for the governance estimate is given by the estimate +/- 1.64 times the standard error.\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_VA_EST (https://data.worldbank.org/indicator/GOV_WGI_VA_EST). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_VA_EST", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "gov_wgi_va_sc": { "title": "Voice and Accountability - Governance score (0-100)", "description_from_producer": "Voice and Accountability (VA) captures perceptions of the extent to which citizens can participate in selecting their government including electoral integrity, and of accountability mechanisms for citizens\u2014reflected in the ability to access information, governmental oversight bodies, and a robust traditional/digital media landscape.\n\nGovernance score is a linear transformation of the governance estimate using hypothetical worst-case and base-case countries. It is an absolute score ranging from 0-100. Larger values correspond to better governance.\n\nThe (country-series-time) metadata include the (a) number of sources, and (b) confidence intervals (lower and upper bound). The number of sources indicates the number of underlying data sources on which the governance score (respectively the underlying governance estimate) is based. The confidence intervals include the lower and upper bound of the 90% confidence interval for the governance score. Confidence intervals are a statistical range of values that likely contain the true value (of what is being estimated).\n\n### Aggregation method:\nIndicators, from the underlying data sources, are aggregated using the Unobserved Components Model (UCM). The UCM is essentially a weighted average, resulting in a governance estimate at the country level. The following provides a detailed description of the aggregation methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%\n\n### Statistical concept and methodology:\nMethodology: The following are the key steps:\nSTEP 1: Assigning indicators from the underlying sources to the six governance dimensions. Individual questions or variables from the underlying data sources are mapped to up to two of the six governance dimensions.\nSTEP 2: Rescaling the individual source data to range from 0 to 1. Each question from the underlying data sources is rescaled to range from 0 to 1, with higher values corresponding to better governance outcomes.\nSTEP 3: Using an Unobserved Components Model to construct a governance estimate for each dimension by taking a weighted average of the source-by-dimension data. To aggregate data across multiple sources, the WGI uses a statistical technique known as an Unobserved Components Model (UCM).\nSTEP 4: Transforming the UCM-generated governance estimates to a 0\u2013100 absolute governance score. The WGI transform the governance estimates for each country, year, and dimension\u2014typically ranging from approximately -2.5 to 2.5 \u2014into absolute scores on a 0-100 scale, with 100 representing the best absolute governance performance.\n\nThe following is a summary of the methodology:\nhttps://www.worldbank.org/en/publication/worldwide-governance-indicators/documentation#3\nThe following is a detailed description of the methodology:\nhttps://www.worldbank.org/content/dam/sites/govindicators/doc/The%20Worldwide%20Governance%20Indicators%202025%20Methodology%20Revision.pdf\nStatistical concept(s): For each of the six governance dimensions, the following are produced: (i) a governance estimate in statistical units (approximately Normal, ranging from -2.5 to 2.5) and (ii) a linear transformation of the governance estimate into an absolute score on a 0-100 scale (anchored by benchmark countries).\n\n### Development relevance:\nGovernance\u2014the traditions and institutions by which authority in a country is exercised\u2014is widely recognized as a critical driver of development outcomes. Theoretical and empirical research shows that countries with inclusive and accountable institutions tend to achieve higher levels of economic development, better public service delivery, and more robust job creation. Conversely, weak governance\u2014marked by corruption, poor regulatory enforcement, or lack of rule of law\u2014 often impedes growth and undermines social outcomes (Acemoglu and Robinson, 2012; World Bank, 2017).\n\nThe Worldwide Governance Indicators (WGI) are designed to help researchers and analysts assess broad patterns in perceptions of governance across countries and over time. The WGI cover more than 200 economies, measuring six dimensions of governance since 1996: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption.\n\nThe WGI provides two complementary outputs: (i) governance estimates expressed in a standard statistical unit, and (ii) scores on an absolute 0-100 scale anchored to fixed reference points. Historical estimates have been recalculated back to 1996 to ensure full consistency over time.\n\nPlease note that the 2025 edition of the WGI introduced a set of methodological updates. These include enhancements to data source screening, indicator mapping, and the aggregation model, as well as the introduction of an absolute 0-100 scale (percentile ranks before) anchored to fixed benchmark countries.\n\n### Limitations and exceptions:\nThe WGI are perception-based measures which draw on 35 distinct underlying data sources, namely expert assessments and household/firm surveys. Data sources based on expert assessments have both strengths and limitations relative to surveys. A key strength is that they are well suited to cross-country comparison, since their methodologies are explicitly designed for this purpose. Expert assessments can also provide more detailed technical judgments on specific public institutions or governance functions that typical household or firm survey respondents may not be well placed to assess. In addition, they may be less affected by respondent reticence, which can arise in household and firm surveys when questions concern sensitive topics such as corruption or other dimensions of governance. At the same time, expert assessments reflect the views of a narrower group of respondents than household or firm surveys. There is also a risk that ratings from one expert assessment may partly reflect information or judgments from other expert assessments, reducing the independence of the underlying signals. To help address this concern, the WGI exclude expert assessments that are explicitly based on other existing data sources.\n\nRegarding the usage of the published WGI data, the six composite WGI measures are useful for broad cross-country comparisons and for assessing general trends over time. However, they are not designed to guide the formulation of specific governance reforms in particular country contexts. Such reforms, and evaluation of their progress, need to be informed by much more detailed and country-specific diagnostic data that can identify the relevant constraints on governance in particular country circumstances. The WGI are complementary to a large number of other efforts to construct more detailed measures of governance, often just for a single country. Users are also encouraged to consult the disaggregated individual indicators underlying the composite WGI scores to gain more insights into the particular areas of strengths and weaknesses identified by the data.", "origins": [ { "producer": "Worldwide Governance Indicators, World Bank Group (WBG)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data., World Bank Group (WBG), uri: https://www.worldbank.org/en/publication/worldwide-governance-indicators, note: Detailed documentation of the WGI, interactive tools for exploring the data, and full access to the underlying source data available at www.govindicators.org. Worldwide Governance Indicators, 2025 Revision, World Bank (www.govindicators.org), Accessed on 12/15/2025. And: World Bank (2025). \u201cThe Worldwide Governance Indicators: Revised Methodology for Measuring Governance Using Perception Data.\u201d Washington, DC: World Bank Group. The 2025 revision builds upon the original WGI methodology which was introduced in 1999 and summarized by the following paper: Kaufmann, Daniel & Aart C. Kraay. (2024). \u201cThe Worldwide Governance Indicators: Methodology and 2024 Update.\u201d Policy Research Working Paper. Washington, DC: World Bank Group., date accessed: 2026-06-03, date published: 2025. Indicator GOV_WGI_VA_SC (https://data.worldbank.org/indicator/GOV_WGI_VA_SC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/GOV_WGI_VA_SC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_educ_fe": { "title": "Human capital index plus (HCI+): education pillar score, female (scale 0\u2013188)", "description_from_producer": "The human capital index plus (HCI+):\u00a0education pillar score aggregates human capital accumulated during formal schooling. This includes pre-school, primary, secondary, and tertiary schooling. The measure also captures learning quality through harmonized learning outcomes.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ education pillar combines three measures of quantity and quality of learning: (1) expected years of schooling (EYS) \u2014 the number of years a child born today can expect to complete by age 18, constructed by summing age-appropriate enrollment rates (approximating ages before the official primary age with pre-primary, 6\u201311 with primary, 12\u201314 with lower secondary, and 15\u201317 with upper secondary) using UNESCO Institute for Statistics (UIS) data and validated/supplemented by World Bank country teams; (2) quality of schooling \u2014 captured by the Harmonized Learning Outcomes, which brings together and harmonizes scores from major international and regional assessments (TIMSS, PIRLS, PISA, SACMEQ, PASEC, LLECE, PILNA) and early-grade reading assessments (EGRAs); and (3) tertiary completion \u2014 the share of young adults who complete tertiary education (measured as the percentage of 25\u201329-year-olds with tertiary credentials), drawn from the WIDE database and national surveys.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_EDUC_FE (https://data.worldbank.org/indicator/HD_HCIP_EDUC_FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_EDUC_FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_educ_ma": { "title": "Human capital index plus (HCI+): education pillar score, male (scale 0\u2013188)", "description_from_producer": "The human capital index plus (HCI+):\u00a0education pillar score aggregates human capital accumulated during formal schooling. This includes pre-school, primary, secondary, and tertiary schooling. The measure also captures learning quality through harmonized learning outcomes.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ education pillar combines three measures of quantity and quality of learning: (1) expected years of schooling (EYS) \u2014 the number of years a child born today can expect to complete by age 18, constructed by summing age-appropriate enrollment rates (approximating ages before the official primary age with pre-primary, 6\u201311 with primary, 12\u201314 with lower secondary, and 15\u201317 with upper secondary) using UNESCO Institute for Statistics (UIS) data and validated/supplemented by World Bank country teams; (2) quality of schooling \u2014 captured by the Harmonized Learning Outcomes, which brings together and harmonizes scores from major international and regional assessments (TIMSS, PIRLS, PISA, SACMEQ, PASEC, LLECE, PILNA) and early-grade reading assessments (EGRAs); and (3) tertiary completion \u2014 the share of young adults who complete tertiary education (measured as the percentage of 25\u201329-year-olds with tertiary credentials), drawn from the WIDE database and national surveys.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_EDUC_MA (https://data.worldbank.org/indicator/HD_HCIP_EDUC_MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_EDUC_MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_educ_to": { "title": "Human capital index plus (HCI+): education pillar score, total (scale 0\u2013188)", "description_from_producer": "The human capital index plus (HCI+):\u00a0education pillar score aggregates human capital accumulated during formal schooling. This includes pre-school, primary, secondary, and tertiary schooling. The measure also captures learning quality through harmonized learning outcomes.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ education pillar combines three measures of quantity and quality of learning: (1) expected years of schooling (EYS) \u2014 the number of years a child born today can expect to complete by age 18, constructed by summing age-appropriate enrollment rates (approximating ages before the official primary age with pre-primary, 6\u201311 with primary, 12\u201314 with lower secondary, and 15\u201317 with upper secondary) using UNESCO Institute for Statistics (UIS) data and validated/supplemented by World Bank country teams; (2) quality of schooling \u2014 captured by the Harmonized Learning Outcomes, which brings together and harmonizes scores from major international and regional assessments (TIMSS, PIRLS, PISA, SACMEQ, PASEC, LLECE, PILNA) and early-grade reading assessments (EGRAs); and (3) tertiary completion \u2014 the share of young adults who complete tertiary education (measured as the percentage of 25\u201329-year-olds with tertiary credentials), drawn from the WIDE database and national surveys.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_EDUC_TO (https://data.worldbank.org/indicator/HD_HCIP_EDUC_TO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_EDUC_TO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_hlth_fe": { "title": "Human capital index plus (HCI+): health pillar score, female (scale 0\u201350)", "description_from_producer": "The human capital index plus (HCI+):\u00a0health pillar score captures productivity effects of surviving to working age and achieving adequate early-life physical growth.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ health pillar combines two measures that together capture the latent health and nutrition conditions of a country: (1) adult survival rate, 15-60, calculated from UN Population Division mortality tables; (2) the fraction of children under five who are not stunted (height-for-age = -2 SD), a marker of adequate early nutrition and growth (Joint child malnutrition estimates (JME)). While there is no single, widely accepted measure of health status, rates of both adult survival & stunting have been linked to population-level measures of latent health. These population-level measures of health, in turn, have been linked to worker productivity.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_HLTH_FE (https://data.worldbank.org/indicator/HD_HCIP_HLTH_FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_HLTH_FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_hlth_ma": { "title": "Human capital index plus (HCI+): health pillar score, male (scale 0\u201350)", "description_from_producer": "The human capital index plus (HCI+):\u00a0health pillar score captures productivity effects of surviving to working age and achieving adequate early-life physical growth.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ health pillar combines two measures that together capture the latent health and nutrition conditions of a country: (1) adult survival rate, 15-60, calculated from UN Population Division mortality tables; (2) the fraction of children under five who are not stunted (height-for-age = -2 SD), a marker of adequate early nutrition and growth (Joint child malnutrition estimates (JME)). While there is no single, widely accepted measure of health status, rates of both adult survival & stunting have been linked to population-level measures of latent health. These population-level measures of health, in turn, have been linked to worker productivity.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_HLTH_MA (https://data.worldbank.org/indicator/HD_HCIP_HLTH_MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_HLTH_MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_hlth_to": { "title": "Human capital index plus (HCI+): health pillar score, total (scale 0\u201350)", "description_from_producer": "The human capital index plus (HCI+):\u00a0health pillar score captures productivity effects of surviving to working age and achieving adequate early-life physical growth.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ health pillar combines two measures that together capture the latent health and nutrition conditions of a country: (1) adult survival rate, 15-60, calculated from UN Population Division mortality tables; (2) the fraction of children under five who are not stunted (height-for-age = -2 SD), a marker of adequate early nutrition and growth (Joint child malnutrition estimates (JME)). While there is no single, widely accepted measure of health status, rates of both adult survival & stunting have been linked to population-level measures of latent health. These population-level measures of health, in turn, have been linked to worker productivity.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_HLTH_TO (https://data.worldbank.org/indicator/HD_HCIP_HLTH_TO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_HLTH_TO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_otjl_fe": { "title": "Human capital index plus (HCI+): on-the-job learning pillar score, female (scale -30\u201387)", "description_from_producer": "The human capital index plus (HCI+):\u00a0on-the-job learning pillar score measures human capital accumulation after age 18 through work experience. The measure captures labor force participation, unemployment, and wage employment for youth and adults.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ on-the-job learning pillar combines three measures that capture the capacity to work and the quality of jobs for both the youth population ages 18-24 and the adult population ages 25\u201365 (1) the share of youth and adults in the labor force \u2014 the labor-force participation metric that reflects attachment to the labor market (ILOSTAT); (2) the share of youths and adults not in unemployment \u2014 based on unemployment rates from ILOSTAT; and (3) the share of youth and adults employed in wage versus non-wage work \u2014 the proportion of workers in paid, salaried jobs versus self-employment or unpaid family work (ILOSTAT\u2019s modeled series). Together, these indicators capture both how long people can participate in the labor market and whether they have access to quality employment opportunities that grow human capital.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_OTJL_FE (https://data.worldbank.org/indicator/HD_HCIP_OTJL_FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_OTJL_FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_otjl_ma": { "title": "Human capital index plus (HCI+): on-the-job learning pillar score, male (scale -30\u201387)", "description_from_producer": "The human capital index plus (HCI+):\u00a0on-the-job learning pillar score measures human capital accumulation after age 18 through work experience. The measure captures labor force participation, unemployment, and wage employment for youth and adults.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ on-the-job learning pillar combines three measures that capture the capacity to work and the quality of jobs for both the youth population ages 18-24 and the adult population ages 25\u201365 (1) the share of youth and adults in the labor force \u2014 the labor-force participation metric that reflects attachment to the labor market (ILOSTAT); (2) the share of youths and adults not in unemployment \u2014 based on unemployment rates from ILOSTAT; and (3) the share of youth and adults employed in wage versus non-wage work \u2014 the proportion of workers in paid, salaried jobs versus self-employment or unpaid family work (ILOSTAT\u2019s modeled series). Together, these indicators capture both how long people can participate in the labor market and whether they have access to quality employment opportunities that grow human capital.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_OTJL_MA (https://data.worldbank.org/indicator/HD_HCIP_OTJL_MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_OTJL_MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_otjl_to": { "title": "Human capital index plus (HCI+): on-the-job learning pillar score, total (scale -30\u201387)", "description_from_producer": "The human capital index plus (HCI+):\u00a0on-the-job learning pillar score measures human capital accumulation after age 18 through work experience. The measure captures labor force participation, unemployment, and wage employment for youth and adults.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ on-the-job learning pillar combines three measures that capture the capacity to work and the quality of jobs for both the youth population ages 18-24 and the adult population ages 25\u201365 (1) the share of youth and adults in the labor force \u2014 the labor-force participation metric that reflects attachment to the labor market (ILOSTAT); (2) the share of youths and adults not in unemployment \u2014 based on unemployment rates from ILOSTAT; and (3) the share of youth and adults employed in wage versus non-wage work \u2014 the proportion of workers in paid, salaried jobs versus self-employment or unpaid family work (ILOSTAT\u2019s modeled series). Together, these indicators capture both how long people can participate in the labor market and whether they have access to quality employment opportunities that grow human capital.\n\n\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_OTJL_TO (https://data.worldbank.org/indicator/HD_HCIP_OTJL_TO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_OTJL_TO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_ovrl_fe": { "title": "Human capital index plus (HCI+): overall score, female (scale 0\u2013325)", "description_from_producer": "The human capital index plus (HCI+): overall score calculates the contributions of health, education, and on-the-job learning to worker productivity. The index measures the productivity of a child born today as a future worker relative to the benchmark of full health, complete education, and full employment in wage employment.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ pulls together 11 outcomes focused on health, education, and employment, and employs the latest research on how each affects earnings. As a result, improvements in the HCI+ can be directly interpreted as increases in workers' lifetime earnings and in GDP over the long run. The scale for the HCI+ is based on the human capital earnings function (originally developed by Mincer (1974) and others), where human capital is measured as the log of lifetime earnings. In this framework, a one-unit change in the index corresponds to a proportional change in earnings. To make the index easier to interpret, we multiply the log measure by 100.\n\nThis means that point differences in HCI+ can be read as approximate percentage differences in lifetime earnings. For example, an increase of 10 points in HCI+ corresponds roughly to a 10 percent increase in expected adult wages (and, in the long run, GDP per worker). Multiplying by 100, therefore, transforms the log measure into policy-relevant percentage units without altering the index's underlying economics.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_OVRL_FE (https://data.worldbank.org/indicator/HD_HCIP_OVRL_FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_OVRL_FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_ovrl_ma": { "title": "Human capital index plus (HCI+): overall score, male (scale 0\u2013325)", "description_from_producer": "The human capital index plus (HCI+): overall score calculates the contributions of health, education, and on-the-job learning to worker productivity. The index measures the productivity of a child born today as a future worker relative to the benchmark of full health, complete education, and full employment in wage employment.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ pulls together 11 outcomes focused on health, education, and employment, and employs the latest research on how each affects earnings. As a result, improvements in the HCI+ can be directly interpreted as increases in workers' lifetime earnings and in GDP over the long run. The scale for the HCI+ is based on the human capital earnings function (originally developed by Mincer (1974) and others), where human capital is measured as the log of lifetime earnings. In this framework, a one-unit change in the index corresponds to a proportional change in earnings. To make the index easier to interpret, we multiply the log measure by 100.\n\nThis means that point differences in HCI+ can be read as approximate percentage differences in lifetime earnings. For example, an increase of 10 points in HCI+ corresponds roughly to a 10 percent increase in expected adult wages (and, in the long run, GDP per worker). Multiplying by 100, therefore, transforms the log measure into policy-relevant percentage units without altering the index's underlying economics.\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_OVRL_MA (https://data.worldbank.org/indicator/HD_HCIP_OVRL_MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_OVRL_MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "hd_hcip_ovrl_to": { "title": "Human capital index plus (HCI+): overall score, total (scale 0\u2013325)", "description_from_producer": "The human capital index plus (HCI+): overall score calculates the contributions of health, education, and on-the-job learning to worker productivity. The index measures the productivity of a child born today as a future worker relative to the benchmark of full health, complete education, and full employment in wage employment.\n\n### Periodicity:\nFive-Year Interval\n\n### Statistical concept and methodology:\nMethodology: The HCI+ pulls together 11 outcomes focused on health, education, and employment, and employs the latest research on how each affects earnings. As a result, improvements in the HCI+ can be directly interpreted as increases in workers' lifetime earnings and in GDP over the long run. The scale for the HCI+ is based on the human capital earnings function (originally developed by Mincer (1974) and others), where human capital is measured as the log of lifetime earnings. In this framework, a one-unit change in the index corresponds to a proportional change in earnings. To make the index easier to interpret, we multiply the log measure by 100.\n\nThis means that point differences in HCI+ can be read as approximate percentage differences in lifetime earnings. For example, an increase of 10 points in HCI+ corresponds roughly to a 10 percent increase in expected adult wages (and, in the long run, GDP per worker). Multiplying by 100, therefore, transforms the log measure into policy-relevant percentage units without altering the index's underlying economics.\n\n\nStatistical concept(s): The HCI+ is a composite indicator that combines three pillars\u2014health, education, and on-the-job learning\u2014into a single measure ranging from 0 to 325. The health pillar assesses adult survival rates and the fraction of children under five who are not stunted, reflecting overall health and nutrition, with a range of 0 to 50. The education pillar measures expected years of schooling, quality of learning through harmonized assessment outcomes, and tertiary education completion rates, with a range of 0 to 188. The on-the-job learning pillar examines labor force participation, unemployment rates, and the share of workers in wage employment among youth and adults, with scores ranging from -30 to 87. A negative value indicates that prolonged unemployment can decrease an individual's human capital. Collectively, these three pillars provide a comprehensive view of a country's human capital and its implications for future economic growth.\n\nReferences: Decerf, Beno\u00eet; D\u2019Souza, Ritika; Schady, Norbert; Silva, Joana. 2026. The Human Capital Index Plus 2026: Methodology Note. \u00a9 World Bank. http://hdl.handle.net/10986/44306 License: CC BY-NC 3.0 IGO.\n\nWorld Bank. 2026. The Human Capital Index Plus 2026. Findings Brief. \u00a9 World Bank. http://hdl.handle.net/10986/44305 License: CC BY-NC 3.0 IGO.\n\n### Development relevance:\nHuman capital consists of the knowledge, skills, and health that people invest in and accumulate throughout their lives, enabling them to realize their potential as productive members of society. Investing in people through nutrition, health care, quality education, jobs, and skills development helps build human capital, which is key to ending extreme poverty and creating more inclusive societies. There is mounting evidence that unless they strengthen their human capital, countries cannot achieve sustained, inclusive economic growth, will not have a workforce prepared for the more highly skilled jobs of the future, and will not compete effectively in the global economy. The cost of inaction on human capital development is going up. Finance Ministers who have been meeting to discuss human capital at recent Spring and Annual Meetings of the World Bank Group have emphasized the importance of human capital to the jobs and economic transformation agenda in countries at all stages of development.", "origins": [ { "producer": "World Bank based on the Human Capital Project", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff calculations based on the Human Capital Project, World Bank (WB), uri: https://humancapital.worldbank.org/en/home. Indicator HD_HCIP_OVRL_TO (https://data.worldbank.org/indicator/HD_HCIP_OVRL_TO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/HD_HCIP_OVRL_TO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_be_os": { "title": "B-READY: Business Entry: Overall Score", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nRegistered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BE.OS (https://data.worldbank.org/indicator/IC.BRE.BE.OS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BE.OS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_be_p1": { "title": "B-READY: Business Entry Pillar 1: Quality of Regulations for Business Entry", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The first pillar assesses the quality of regulations for business entry, covering de jure features of a regulatory framework that are necessary for the adoption of good practices for business start-ups.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nRegistered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BE.P1 (https://data.worldbank.org/indicator/IC.BRE.BE.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BE.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_be_p2": { "title": "B-READY: Business Entry Pillar 2: Digital Public Services and Transparency of Information for Business Entry", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The second pillar measures the availability of digital public services and transparency of information for business entry.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nRegistered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BE.P2 (https://data.worldbank.org/indicator/IC.BRE.BE.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BE.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_be_p3": { "title": "B-READY: Business Entry Pillar 3: Operational Efficiency of Business Entry", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Entry topic measures the process of registration and start of operations of new limited liability companies (LLCs) across three different dimensions, or pillars. The third pillar measures the time and cost required to register new domestic and foreign firms.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nRegistered companies benefit from a variety of advantages, including the legal and financial services provided by courts and banks. Their employees enjoy social security protection. Additionally, the economy takes advantage of positive spillovers: where formal entrepreneurship is high, job creation and economic growth also tend to be high. As more businesses formalize, the tax base also expands, enabling the government to spend on productivity-enhancing areas and pursue other social and economic policy goals. However, entrepreneurs often encounter barriers to entry into the formal economy.\n\nThere is evidence that higher costs for business start-ups are associated with lower business entry and lower levels of employment and productivity. Cumbersome regulations for business start-ups are associated with high levels of corruption and informality. A simple business start-up process is a positive factor for fostering formal entrepreneurship. Moreover, digital technology and transparency of information can encourage businesses to register and promote private sector growth.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BE.P3 (https://data.worldbank.org/indicator/IC.BRE.BE.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BE.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_bi_os": { "title": "B-READY: Business Insolvency: Overall Score", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nThe efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies\u2019 economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BI.OS (https://data.worldbank.org/indicator/IC.BRE.BI.OS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BI.OS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_bi_p1": { "title": "B-READY: Business Insolvency Pillar 1: Quality of Regulations for Judicial Insolvency Proceedings", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The first pillar assesses the quality of regulation pertaining to judicial insolvency proceedings\u2014liquidation and reorganization, covering de jure features of a regulatory framework that are necessary for structured debt resolution processes and effective creditor and debtor regimes.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nThe efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies\u2019 economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BI.P1 (https://data.worldbank.org/indicator/IC.BRE.BI.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BI.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_bi_p2": { "title": "B-READY: Business Insolvency Pillar 2: Quality of Institutional and Operational Infrastructure for Judicial Insolvency Proceedings", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The second pillar measures the quality of institutional and operational infrastructure for judicial insolvency proceedings, thus assessing the de facto aspects of insolvency resolution mechanisms and the infrastructure required to implement the legal framework on insolvency.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nThe efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies\u2019 economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BI.P2 (https://data.worldbank.org/indicator/IC.BRE.BI.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BI.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_bi_p3": { "title": "B-READY: Business Insolvency Pillar 3: Operational Efficiency of Resolving Judicial Insolvency Proceedings", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Insolvency topic measures key features of insolvency systems on a regulatory level. It also assesses the institutional and operational infrastructure associated with insolvency proceedings (judicial services), as well as the operational efficiency of insolvency proceedings across three different dimensions, or pillars. The third pillar measures the time and cost required to resolve in-court liquidation and reorganization proceedings.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nThe efficient and rapid exit of nonviable firms plays an important cyclical role in renewing the economy by removing firms that are not productive and making way for more productive ones. The purpose of an efficient insolvency framework is to ensure that nonviable firms are swiftly liquidated, and viable firms are effectively restructured in a sustainable way. When insolvency regimes do not have the adequate tools to handle the restructuring and liquidation of companies in a timely and effective manner these companies\u2019 economic distress is amplified, jeopardizing the stability of the financial system. In economies where creditor recovery rates are high and resolution times are quicker, restructuring within the formal bankruptcy process fulfills its cyclical role during economic downturns by keeping companies afloat.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BI.P3 (https://data.worldbank.org/indicator/IC.BRE.BI.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BI.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_bl_os": { "title": "B-READY: Business Location: Overall Score", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options\u2014purchase, lease, or build\u2014that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nAcquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy\u2019s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development.\n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BL.OS (https://data.worldbank.org/indicator/IC.BRE.BL.OS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BL.OS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_bl_p1": { "title": "B-READY: Business Location Pillar 1: Quality of Regulations for Business Location", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options\u2014purchase, lease, or build\u2014that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The first pillar assesses the quality of regulations pertaining to property transfer, building, and environmental permitting, covering de jure features of a regulatory framework that are necessary for immovable property lease, property ownership, urban planning, and environmental licenses.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nAcquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy\u2019s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development.\n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BL.P1 (https://data.worldbank.org/indicator/IC.BRE.BL.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BL.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_bl_p2": { "title": "B-READY: Business Location Pillar 2: Quality of Public Services and Transparency of Information for Business Location", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options\u2014purchase, lease, or build\u2014that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The second pillar assesses the quality of public services and transparency of information in the provision of property transfer, building, and environmental permitting.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nAcquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy\u2019s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development.\n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BL.P2 (https://data.worldbank.org/indicator/IC.BRE.BL.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BL.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_bl_p3": { "title": "B-READY: Business Location Pillar 3: Operational Efficiency of Establishing a Business Location", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Business Location topic measures three different options\u2014purchase, lease, or build\u2014that are available to entrepreneurs to choose the adequate location to set up their company, across three different dimensions, or pillars. The third pillar measures the operational efficiency of establishing a business location in practice.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nAcquiring the physical space where a business will operate is a crucial ingredient of success for many firms, even in the digital age. Getting the right location can influence business access to customers, transportation, labor, and materials, as well as determine taxes, regulations, and environmental commitments they must comply with.\n\nWhether an entrepreneur is leasing or purchasing a commercial property, the regulatory framework and the public services related to acquiring a location can have an impact on how conducive the business environment is for individual firms and the private sector development of an economy.\n\nFirms are more likely to invest in economies with strong property rights in which they are confident that their immovable property investments will be safe. A reliable land administration system, which provides clear information on property ownership, facilitates the development of real estate markets, and supports tenure security. These factors not only provide confidence to the private sector but also indicate the economy\u2019s prospects for economic growth. Transparency in land administration also reduces information asymmetries, which increases market efficiency, further supporting economic development.\n\nWhen investors and entrepreneurs acquire a new location for their business, the process often involves licensing requirements for altering a property or changing tenancy. Building-related permits are essential for public safety, strengthening property rights, and contributing to capital formation. Last but not least, transparent and accessible environmental regulations related to building control reduce the regulatory burden on firms by offering clarity on rules and regulations.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.BL.P3 (https://data.worldbank.org/indicator/IC.BRE.BL.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.BL.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_dr_os": { "title": "B-READY: Dispute Resolution: Overall Score", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes\u2014those arising in the business context between firms\u2014across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nInability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries\u2019 effectiveness but also their strength and reliability.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.DR.OS (https://data.worldbank.org/indicator/IC.BRE.DR.OS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.DR.OS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_dr_p1": { "title": "B-READY: Dispute Resolution Pillar 1: Quality of Regulations for Dispute Resolution", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes\u2014those arising in the business context between firms\u2014across three different dimensions, or pillars. The first pillar assesses the adequacy of legislation pertaining to both court processes and alternative dispute resolution (ADR), covering de jure features that are necessary for the efficient processing of cases, facilitated resolution of cross-border claims, creating alternative venues for settling disputes, and ensuring trust in relevant institutions.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nInability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries\u2019 effectiveness but also their strength and reliability.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.DR.P1 (https://data.worldbank.org/indicator/IC.BRE.DR.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.DR.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_dr_p2": { "title": "B-READY: Dispute Resolution Pillar 2: Public Services for Dispute Resolution", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes\u2014those arising in the business context between firms\u2014across three different dimensions, or pillars. The second pillar focuses on judicial organizational structure, courts\u2019 digitization and transparency, as well as ADR-related services, thus capturing the de facto provision of public services.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nInability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries\u2019 effectiveness but also their strength and reliability.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.DR.P2 (https://data.worldbank.org/indicator/IC.BRE.DR.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.DR.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_dr_p3": { "title": "B-READY: Dispute Resolution Pillar 3: Ease of Resolving a Commercial Dispute", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Dispute Resolution topic measures efficiency and quality of the resolution of commercial disputes\u2014those arising in the business context between firms\u2014across three different dimensions, or pillars. The third pillar measures the reliability of dispute resolution, the time and cost required to resolve a dispute, as well as the time and cost associated with the recognition and enforcement of decisions.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nInability to resolve commercial disputes promptly and fairly can lead to adverse economic outcomes in the private sector, ranging from reduced entrepreneurship and lower investment to macroeconomic volatility. This makes efficient and quality dispute resolution essential for a healthy business environment. Having time- and cost-effective mechanisms for resolving disputes is critical because excessively long and expensive proceedings may defeat the very purpose of bringing a case to formal institutions, making them unattractive and unaffordable. In fact, correlations have been established between judicial efficiency and facilitated entrepreneurial activity. Evidence also suggests that under a more effective court system businesses are likely to have greater access to finance and borrow more. The quality of the dispute resolution process also matters. Claims should be considered with due care by credible institutions capable of issuing sound judgments. It was found that in economies with low confidence in court systems, firms are less willing to expand their businesses and look for alternative trade partners. To attract more investors, economies therefore should ensure not only judiciaries\u2019 effectiveness but also their strength and reliability.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.DR.P3 (https://data.worldbank.org/indicator/IC.BRE.DR.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.DR.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_fs_os": { "title": "B-READY: Financial Services: Overall Score", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas\u2014Commercial Lending; Secured Transactions; e-Payments; and Credit Information\u2014across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nAccess to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms\u2019 ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company\u2019s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms\u2019 financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies\u2019 ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.FS.OS (https://data.worldbank.org/indicator/IC.BRE.FS.OS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.FS.OS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_fs_p1": { "title": "B-READY: Financial Services Pillar 1: Quality of Regulations for Financial Services", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas\u2014Commercial Lending; Secured Transactions; e-Payments; and Credit Information\u2014across three different dimensions, or pillars. The first pillar assesses the effectiveness of regulation pertaining to commercial lending, secured transactions, and e-payments, covering the de jure features of regulatory frameworks.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nAccess to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms\u2019 ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company\u2019s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms\u2019 financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies\u2019 ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.FS.P1 (https://data.worldbank.org/indicator/IC.BRE.FS.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.FS.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_fs_p2": { "title": "B-READY: Financial Services Pillar 2: Accessibility of Information in Credit Infrastructure", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas\u2014Commercial Lending; Secured Transactions; e-Payments; and Credit Information\u2014across three different dimensions, or pillars. The second pillar measures the accessibility of information in credit infrastructure by evaluating the operation of credit bureaus and registries and the operation of collateral registries. Thus, the second pillar assesses de facto and some de jure aspects of financial services.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nAccess to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms\u2019 ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company\u2019s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms\u2019 financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies\u2019 ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.FS.P2 (https://data.worldbank.org/indicator/IC.BRE.FS.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.FS.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_fs_p3": { "title": "B-READY: Financial Services Pillar 3: Operational Efficiency of Receiving Financial Services", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Financial Services topic measures four areas\u2014Commercial Lending; Secured Transactions; e-Payments; and Credit Information\u2014across three different dimensions, or pillars. The third pillar measures the operational efficiency of (i) obtaining a loan; (ii) registering a security interest as well as the timeliness of credit information sharing; and (iii) e-payments usage and their efficiency.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nAccess to finance remains a major constraint for firms worldwide, despite being essential for their operations and expansion and positively associated with innovation. Additionally, access to finance affects firms\u2019 ability to manage a volatile cash flow and directly contributes to their resilience, which was underscored during the global pandemic. Research has also shown that private sector financing in developing economies has positive macroeconomic effects as firm-level employment often benefits from improved access to finance.\n\nAccess to finance also plays an important role in maintaining a company\u2019s financial stability. Removing bottlenecks associated with making and receiving payments further strengthens firms\u2019 financial security. In recent years, cashless transactions (including e-payments) have continued growing. However, economies\u2019 ever-increasing digitalization requires modern regulations that enable electronic solutions to reap the benefits of technological progress. This would unlock the extensive use of electronic payments (e-payments), which is associated with reduced tax evasion and lower informality in the private sector.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.FS.P3 (https://data.worldbank.org/indicator/IC.BRE.FS.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.FS.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_it_os": { "title": "B-READY: International Trade: Overall Score", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade\u2014trade in goods, trade in services, and digital trade\u2014across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nInternational trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.IT.OS (https://data.worldbank.org/indicator/IC.BRE.IT.OS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.IT.OS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_it_p1": { "title": "B-READY: International Trade Pillar 1: Quality of Regulations for International Trade", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade\u2014trade in goods, trade in services, and digital trade\u2014across three different dimensions, or pillars. The first pillar assesses the quality of regulations pertaining to international trade, covering de jure features of a regulatory framework that are necessary to establish a nondiscriminatory, transparent, predictable, and safe environment to harness the potential of international trade.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nInternational trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.IT.P1 (https://data.worldbank.org/indicator/IC.BRE.IT.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.IT.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_it_p2": { "title": "B-READY: International Trade Pillar 2: Quality of Public Services for the Facilitation of International Trade", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade\u2014trade in goods, trade in services, and digital trade\u2014across three different dimensions, or pillars. The second pillar assesses digital and physical infrastructure concerning international trade and the quality of border management, thus assessing de facto provision of public services for international trade facilitation.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nInternational trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.IT.P2 (https://data.worldbank.org/indicator/IC.BRE.IT.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.IT.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_it_p3": { "title": "B-READY: International Trade Pillar 3: Efficiency of Importing Goods, Exporting Goods, and Engaging in Digital Trade", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The International Trade topic measures different aspects of international trade\u2014trade in goods, trade in services, and digital trade\u2014across three different dimensions, or pillars. The third pillar measures the time and cost to comply with export and import requirements, participation in cross-border digital trade, as well as the perceived major obstacles for international trade.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nInternational trade is a key driver of economic growth and private sector development. Through competition among domestic and foreign firms, it promotes specialization and resource reallocation to the most productive firms. While there are winners and losers among firms, workers, and consumers, international trade can generate overall benefits for the private sector and society. To remain competitive, firms must continuously adapt, innovate, and improve their efficiency, resulting in aggregate productivity growth and welfare. Trade openness may generate further productivity gains by creating economies of scale and providing access to cheaper intermediate inputs of higher quality and variety, as well as facilitating knowledge and technology transfers. Increased access to foreign inputs may enhance productivity and export performance, and it may provide opportunities to diversify the economy and reduce its dependence on a single product or market. This shows the complementarities between exports and imports and emphasizes the importance of trade openness to reap the benefits of international trade.\n\nTo fully realize the benefits of international trade, it is necessary to have a conducive business environment that reduces trade barriers and lowers compliance and transaction costs for the private sector. A regulatory framework that establishes a nondiscriminatory, transparent, predictable, and safe trading environment generates incentives to engage in international trade and provides a level playing field. Furthermore, it is crucial to have regulations that strike a balance between public policy objectives, including protecting public health and the environment, and the requirements they impose, which can create market distortions that impede trade. Finally, policies that improve the quality of physical and digital infrastructure, as well as border management, reduce the time and cost borne by the private sector, which represents a substantial barrier to trade, and increase participation in international trade for small, medium, and large firms.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.IT.P3 (https://data.worldbank.org/indicator/IC.BRE.IT.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.IT.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_lb_os": { "title": "B-READY: Labor: Overall Score", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nLabor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms\u2019 decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter\u2014they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.LB.OS (https://data.worldbank.org/indicator/IC.BRE.LB.OS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.LB.OS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_lb_p1": { "title": "B-READY: Labor Pillar 1: Quality of Labor Regulations", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The first pillar assesses the quality of labor regulations pertaining to workers' conditions and employment restrictions and costs, covering de jure features of the regulatory framework that are necessary for the functioning of the labor market and to provide employers and employees with their obligations and relevant safeguards.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nLabor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms\u2019 decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter\u2014they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.LB.P1 (https://data.worldbank.org/indicator/IC.BRE.LB.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.LB.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_lb_p2": { "title": "B-READY: Labor Pillar 2: Adequacy of Public Services for Labor", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The second pillar measures the adequacy of public services for labor, assessing the de facto provision of social protection and the institutional framework on which the labor market and the enforcement of labor regulations depend.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nLabor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms\u2019 decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter\u2014they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.LB.P2 (https://data.worldbank.org/indicator/IC.BRE.LB.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.LB.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_lb_p3": { "title": "B-READY: Labor Pillar 3: Operational Efficiency of Labor Regulations and Public Services in Practice", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Labor topic measures good practices in employment regulations and public services from the perspective of both enterprises and employees across three different dimensions, or pillars. The third pillar measures the operational efficiency of labor regulations and public services in practice, assessing employment restrictions and cost, as well as public services.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nLabor is arguably the most important factor of production in most businesses and the primary source of income for most people. Regulations and public services related to labor are fundamental drivers of private sector development from the perspective of both enterprises and workers. These regulations and public services affect firms\u2019 decisions on whether to expand by hiring labor, and whether to do so formally or informally. In addition, they impact the well-being of potential workers by providing them with good jobs and opportunities for growth.\n\nFor formally employed workers, labor regulations matter\u2014they protect their rights, reduce the risk of job loss, and promote equity and welfare. For workers employed in the informal sector, labor regulations can affect their ability to enter the formal workforce. If labor regulations make hiring costs too high or the rules too cumbersome, firms may opt to use more capital than labor or to hire informally. Sound and balanced labor regulations are essential for ensuring that both firms and workers benefit from a dynamic and innovative labor market without compromising income security or basic workers' rights. In addition, public services may address market imperfections and have important implications for the functioning of the labor market and firm choices.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.LB.P3 (https://data.worldbank.org/indicator/IC.BRE.LB.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.LB.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_mc_os": { "title": "B-READY: Market Competition: Overall Score", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nThere is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms\u2019 behaviors or government interventions. Market power\u2014a firm\u2019s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition\u2014must be kept in check.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.MC.OS (https://data.worldbank.org/indicator/IC.BRE.MC.OS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.MC.OS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_mc_p1": { "title": "B-READY: Market Competition Pillar 1: Quality of Regulations that Promote Market Competition", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The first pillar assesses the quality of regulations that promote market competition, covering de jure features of a regulatory framework that enable firms to participate in fair market conditions and innovate, and where firms can participate in open and competitive government markets.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nThere is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms\u2019 behaviors or government interventions. Market power\u2014a firm\u2019s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition\u2014must be kept in check.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.MC.P1 (https://data.worldbank.org/indicator/IC.BRE.MC.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.MC.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_mc_p2": { "title": "B-READY: Market Competition Pillar 2: Public Services that Promote Market Competition", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The second pillar measures the adequacy of public services that promote market competition, thus assessing the de facto provision of services that create an equal level of playing field in markets, and that foster and promote innovation.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nThere is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms\u2019 behaviors or government interventions. Market power\u2014a firm\u2019s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition\u2014must be kept in check.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.MC.P2 (https://data.worldbank.org/indicator/IC.BRE.MC.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.MC.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_mc_p3": { "title": "B-READY: Market Competition Pillar 3: Implementation of Key Services Promoting Market Competition", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Market Competition topic measures good practices related to the enforcement of competition policy, intellectual property rights and innovation policy, and regulations that focus on improving competition and innovation in markets where the government is a purchaser of services or goods, across the three different pillars. The third pillar measures the operational efficiency in the implementation of key services promoting market competition (reflecting both the ease of compliance with the regulatory framework and the effective provision of public services directly relevant to firms that contribute in practice to the promotion of market competition).\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nThere is substantial economic evidence that a fair level of market competition spurs economic growth by increasing industry and firm innovation and productivity, leading to better products, more and better jobs, and higher incomes. By affecting market entry and exit, competition stimulates product innovation and service quality, protects consumers, and forces market operators to provide their products and services at cost. But competition is rarely perfect. Markets fail either due to firms\u2019 behaviors or government interventions. Market power\u2014a firm\u2019s ability to raise prices well above cost, offer a low-quality good or service, and drive out competition\u2014must be kept in check.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.MC.P3 (https://data.worldbank.org/indicator/IC.BRE.MC.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.MC.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_p1_rf": { "title": "B-READY: Pillar 1: Regulatory Framework", "description_from_producer": "B-READY assesses the economy\u2019s business environment by focusing on the regulatory framework and the provision of related public services for firms and markets, as well as the operational efficiency with which they are combined in practice. B-READY\u2019s three pillars\u2014the Regulatory Framework, Public Services, and Operational Efficiency. The Regulatory Framework comprises the rules and regulations that firms must follow as they open, operate, and close a business.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nThe business environment can be defined as the set of conditions outside a firm\u2019s control that have a significant influence on how businesses behave and perform throughout their life cycle. This set of conditions can be very large, from macroeconomic policy to microeconomic rules. B-READY concentrates on the regulatory framework and public service provision at the microeconomic level: that is, as enacted and implemented to directly affect firms\u2019 behavior and performance. B-READY assesses an economy\u2019s business environment by focusing on the regulatory framework and the provision of related public services directed at firms and markets, as well as the efficiency with which regulatory framework and public services are combined in practice. B-READY seeks a balanced approach when assessing the business environment: between ease of conducting a business and broader private sector benefits, between regulatory framework and public services, between de jure laws and regulations and de facto practical implementation, and between data representativeness and data comparability. For nearly all indicators, the regulatory framework pillar captures de jure information, and the public services and efficiency pillars capture de facto information.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.P1.RF (https://data.worldbank.org/indicator/IC.BRE.P1.RF). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.P1.RF", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_p2_ps": { "title": "B-READY: Pillar 2: Public Services", "description_from_producer": "B-READY assesses the economy\u2019s business environment by focusing on the regulatory framework and the provision of related public services for firms and markets, as well as the operational efficiency with which they are combined in practice. B-READY\u2019s three pillars\u2014the Regulatory Framework, Public Services, and Operational Efficiency. Public Services refers to both the facilities that governments provide directly or through private firms to support compliance with regulations and the critical institutions and infrastructure that enable business activities. Public services considered by B-READY are limited to the scope of the business environment areas related to the life cycle of the firm.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nThe business environment can be defined as the set of conditions outside a firm\u2019s control that have a significant influence on how businesses behave and perform throughout their life cycle. This set of conditions can be very large, from macroeconomic policy to microeconomic rules. B-READY concentrates on the regulatory framework and public service provision at the microeconomic level: that is, as enacted and implemented to directly affect firms\u2019 behavior and performance. B-READY assesses an economy\u2019s business environment by focusing on the regulatory framework and the provision of related public services directed at firms and markets, as well as the efficiency with which regulatory framework and public services are combined in practice. B-READY seeks a balanced approach when assessing the business environment: between ease of conducting a business and broader private sector benefits, between regulatory framework and public services, between de jure laws and regulations and de facto practical implementation, and between data representativeness and data comparability. For nearly all indicators, the regulatory framework pillar captures de jure information, and the public services and efficiency pillars capture de facto information.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.P2.PS (https://data.worldbank.org/indicator/IC.BRE.P2.PS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.P2.PS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_p3_oe": { "title": "B-READY: Pillar 3: Operational Efficiency", "description_from_producer": "B-READY assesses the economy\u2019s business environment by focusing on the regulatory framework and the provision of related public services for firms and markets, as well as the operational efficiency with which they are combined in practice. B-READY\u2019s three pillars\u2014the Regulatory Framework, Public Services, and Operational Efficiency. Operational Efficiency comprises both the ease of compliance with the regulatory framework and the effective use of public services directly relevant to firms.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nThe business environment can be defined as the set of conditions outside a firm\u2019s control that have a significant influence on how businesses behave and perform throughout their life cycle. This set of conditions can be very large, from macroeconomic policy to microeconomic rules. B-READY concentrates on the regulatory framework and public service provision at the microeconomic level: that is, as enacted and implemented to directly affect firms\u2019 behavior and performance. B-READY assesses an economy\u2019s business environment by focusing on the regulatory framework and the provision of related public services directed at firms and markets, as well as the efficiency with which regulatory framework and public services are combined in practice. B-READY seeks a balanced approach when assessing the business environment: between ease of conducting a business and broader private sector benefits, between regulatory framework and public services, between de jure laws and regulations and de facto practical implementation, and between data representativeness and data comparability. For nearly all indicators, the regulatory framework pillar captures de jure information, and the public services and efficiency pillars capture de facto information.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.P3.OE (https://data.worldbank.org/indicator/IC.BRE.P3.OE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.P3.OE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_tx_os": { "title": "B-READY: Taxation: Overall Score", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nTaxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.TX.OS (https://data.worldbank.org/indicator/IC.BRE.TX.OS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.TX.OS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_tx_p1": { "title": "B-READY: Taxation Pillar 1: Quality of Regulations on Taxation", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The first pillar assesses the quality of regulation related to taxation, encompassing both the legal framework (de jure) and the implementation (de facto) of the legal requirements.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nTaxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.TX.P1 (https://data.worldbank.org/indicator/IC.BRE.TX.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.TX.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_tx_p2": { "title": "B-READY: Taxation Pillar 2: Public Services Provided by the Tax Administration", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The second pillar measures the quality of tax administration by assessing the public services related to tax matters.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nTaxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.TX.P2 (https://data.worldbank.org/indicator/IC.BRE.TX.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.TX.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_tx_p3": { "title": "B-READY: Taxation Pillar 3: Operational Efficiency of Tax Systems in Practice", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Taxation topic measures the quality of regulation, administration, and practical implementation of tax systems across the three different dimensions, or pillars. The third pillar evaluates the practical effectiveness of the implemented tax regulations and public services.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nTaxation is a powerful policy tool that governments use to generate revenue to finance their operations and provide public goods and services. Taxation affects the private sector development through a variety of interrelated channels. On the one hand, it creates enabling conditions for the growth and development of the private sector by financing critical infrastructure, investing in human capital, ensuring law enforcement, and supporting other vital services. On the other hand, excessive taxation can distort markets, impair investment decisions, and foster tax evasion. Likewise, cumbersome regulations, complex tax reporting requirements, and inefficient and inconsistent tax procedures increase compliance costs on firms, thereby discouraging formalization. Identifying the key challenges faced by taxpayers is essential for guiding reforms that support private sector development while pursuing domestic resource mobilization objectives.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.TX.P3 (https://data.worldbank.org/indicator/IC.BRE.TX.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.TX.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_us_os": { "title": "B-READY: Utility Services: Overall Score", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. The overall topic score is generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nBy providing essential services\u2014electricity, water, and digital connectivity\u2014utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today\u2019s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.US.OS (https://data.worldbank.org/indicator/IC.BRE.US.OS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.US.OS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_us_p1": { "title": "B-READY: Utility Services Pillar 1: Quality of Regulations on Utility Services", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. Under the first pillar the Utility Services topic assesses the effectiveness of regulation pertaining to electricity, water, and internet services, covering de jure features of a regulatory framework that are necessary for the efficient deployment of connections, reliable service, safety, and environmental sustainability of provision and use of utility services.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nBy providing essential services\u2014electricity, water, and digital connectivity\u2014utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today\u2019s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.US.P1 (https://data.worldbank.org/indicator/IC.BRE.US.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.US.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_us_p2": { "title": "B-READY: Utility Services Pillar 2: Quality of Governance and Transparency of Utility Services", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. The second pillar of the topic measures the quality of governance and transparency in the provision of utility services, thus assessing the de facto provision of utility services.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nBy providing essential services\u2014electricity, water, and digital connectivity\u2014utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today\u2019s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.US.P2 (https://data.worldbank.org/indicator/IC.BRE.US.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.US.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bre_us_p3": { "title": "B-READY: Utility Services Pillar 3: Operational Efficiency of Utility Service Provision", "description_from_producer": "B-READY focuses on ten topics that are organized following the life cycle of the firm and its participation in the market while opening, operating (or expanding), and closing (or reorganizing) a business. The Utility Services topic measures the effectiveness of regulatory frameworks, and the quality of governance and transparency of service delivery mechanisms, as well as the operational efficiency of providing electricity, water, and internet services. The third pillar measures the time required to obtain electricity, water, and internet connections, as well as the reliability of utility service supply.\n\n### Statistical concept and methodology:\nMethodology: B-READY is designed for benchmarking across economies. This requires the application of a homogeneous methodology across economies at different income levels and in different geographic locations. It also requires quantifying the business environment conditions using indicators that can be aggregated into comparable scores.\nB-READY granular data provide a wealth of information that can be used to guide specific policy reform. These data are presented in the main body of the report and, in more detail, in the Business Ready website through accessible facilities and tools, including economy profiles.\nBased on the data collected, B-READY generates scores for each topic area and potentially a set of aggregate scores. B-READY collects both de jure information and de facto measures. While de jure data are collected from expert consultations, de facto data are collected from both expert consultations and firm surveys.\nAll data obtained from either experts or firms are collected in raw form and then converted to a score that can be combined with other scores. The objective of the scoring methodology of raw data is to allow for score aggregation that preserves absolute cardinal differences, which can be used to compare across economies and over time (rather than purely ordinal or relative scoring). The granular data produced by the B-READY project are combined to produce a score for each of the ten B-READY topics. Every topic score will be generated by averaging the scores assigned to each of the three pillars (regulatory framework, public services, and efficiency) for that topic.\nIn addition, to facilitate international benchmarking, these granular data are used to obtain topic-specific pillar scores, topic scores, and overall pillar scores. A topic-specific pillar score is built from the points assigned to sets of indicators, organized in categories by subject matter. Each score can range from 0 to 100 (where 100 represents the best performance). Within each topic, there are three topic-specific pillars: Regulatory Framework, Public Services, and Operational Efficiency. The average of the three topic-specific pillar scores, in turn, equals the topic score. Each overall pillar score is the average of the corresponding topic-specific pillar scores across the ten B-READY topics.\nDetails about the B-READY methodology are available on the project website: https://www.worldbank.org/en/businessready/methodology#1\n\n### Development relevance:\nBy providing essential services\u2014electricity, water, and digital connectivity\u2014utilities play an important role in supporting economic and social development. Without these services, businesses cannot function, and households cannot lead quality lives. Yet, more than 30 percent of businesses globally identify electricity supply as a major constraint to their operations, according to the World Bank Enterprise Surveys. Disruptions in electricity supply impair firm productivity, revenues, and economic growth. Similarly, inadequate water supply can lead to decreased firm productivity, deterioration of machinery, and reduced profits. Access to reliable internet is another critical element in today\u2019s digitalized world, where the use of digital technologies improve productivity. However, as of 2021, just over 15 percent of people globally had fixed broadband subscriptions, and only 1.4 percent in the least developed countries. The provision of basic utility services should be effective and reliable. Facilitating timely access to such services in an environmentally sustainable manner is instrumental for economic growth.\n\nThe effectiveness of regulatory frameworks, good governance, transparency, and efficiency of utility services are pivotal elements of a good business environment. An effective regulatory framework, for example, is a fundamental steppingstone for the provision of high-quality utility services. Furthermore, the reliability and sustainability of utility services should be maintained through monitoring the quality of service supply and connection safety, fostering public accountability and safety. Interoperability through agency coordination and digitalization of utilities can also help improve the quality of public services and the customer experience.", "origins": [ { "producer": "Business Ready (B-READY) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Business Ready (B-READY) project, World Bank (WB), uri: https://www.worldbank.org/en/businessready. Indicator IC.BRE.US.P3 (https://data.worldbank.org/indicator/IC.BRE.US.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BRE.US.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_bus_ndns_zs": { "title": "New business density (new registrations per 1,000 people ages 15-64)", "description_from_producer": "The number of newly registered firms with limited liability per 1,000 working-age people (ages 15-64) per calendar year.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The Entrepreneurship Database has developed a data collection methodology to systematically measure entrepreneurial activity. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data \u2013 most often due to an absence of digitized registration systems \u2013 the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and publicly available data.\nStatistical concept(s): In order to measure entrepreneurship in a way that is universally comparable, the Entrepreneurship Database employs a methodology that can be applied across heterogeneous legal regimes and economic systems. The concept of entrepreneurship can cover a wide range of activities. For the purposes of this project, entrepreneurship is defined as the activities of an individual or a group of individuals aimed at initiating economic enterprise in the formal sector under a legal form of business.\n\nThe legal form of business refers to private companies with limited liability. Limited liability companies are those in which the financial liability of the firm\u2019s members is limited to the value of their investment in the company. A limited liability company is a separate legal entity that has its own privileges and liabilities. Although the laws on business registration vary greatly across countries, the approach to legal entities is largely uniform: any business with a unique legal entity (or \u201ccorporate personhood\u201d) separate from its owners must be registered.\n\n### Development relevance:\nEntrepreneurial activity is a pillar of economic growth. The Entrepreneurship Database is a critical source of data that facilitates the measurement of entrepreneurial activity across countries and over time. The data also allow for a deeper understanding of the relationship between new firm registration, the regulatory environment, and economic growth. Previous research using the Entrepreneurship Database has shown a significant relationship between the cost of compliance required to start a business and new firm registration.\n\n### Limitations and exceptions:\nThe definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. Data collected from economies categorized as offshore financial centers by Eurostat are excluded from the analysis, because registered entities in these countries may not fit the objective of the Entrepreneurship Database aiming at measuring formally registered companies with actual economic activities. The information provided by these economies likely reflects a number of shell companies, defined as companies that are registered for tax purposes but are not economically active. The information on offshore centers is collected and published by the Entrepreneurship Database, but it is not used for trend analysis purposes.\n\n### Other notes:\nFor cross-country comparability, only limited liability corporations that operate in the formal sector are included.", "origins": [ { "producer": "Entrepreneurship Database, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Entrepreneurship Database, World Bank (WB), uri: https://www.worldbank.org/en/programs/entrepreneurship. Indicator IC.BUS.NDNS.ZS (https://data.worldbank.org/indicator/IC.BUS.NDNS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BUS.NDNS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "new registrations per 1,000 people ages 15-64", "short_unit": "new registrations", "display": { "shortUnit": "new registrations", "unit": "new registrations per 1,000 people ages 15-64" } }, "ic_bus_nreg": { "title": "New businesses registered (number)", "description_from_producer": "New businesses registered are the number of new limited liability corporations (or its equivalent) registered in the calendar year.\n\n### Statistical concept and methodology:\nMethodology: The Entrepreneurship Database has developed a data collection methodology to systematically measure entrepreneurial activity. To facilitate cross-country comparability, the Entrepreneurship Database employs a consistent unit of measurement, source of information, and concept of entrepreneurship that is applicable and available among the diverse sample of participating economies.\n\nThe data collection process involves telephone interviews and email correspondence with business registries. The main sources of information for this study are national business registries. In a limited number of cases where the business registry was unable to provide the data \u2013 most often due to an absence of digitized registration systems \u2013 the Entrepreneurship Database uses other alternatives sources, such as statistical agencies, tax and labor agencies, chambers of commerce, and publicly available data.\nStatistical concept(s): In order to measure entrepreneurship in a way that is universally comparable, the Entrepreneurship Database employs a methodology that can be applied across heterogeneous legal regimes and economic systems. The concept of entrepreneurship can cover a wide range of activities. For the purposes of this project, entrepreneurship is defined as the activities of an individual or a group of individuals aimed at initiating economic enterprise in the formal sector under a legal form of business.\n\nThe legal form of business refers to private companies with limited liability. Limited liability companies are those in which the financial liability of the firm\u2019s members is limited to the value of their investment in the company. A limited liability company is a separate legal entity that has its own privileges and liabilities. Although the laws on business registration vary greatly across countries, the approach to legal entities is largely uniform: any business with a unique legal entity (or \u201ccorporate personhood\u201d) separate from its owners must be registered.\n\n### Development relevance:\nEntrepreneurial activity is a pillar of economic growth. The Entrepreneurship Database is a critical source of data that facilitates the measurement of entrepreneurial activity across countries and over time. The data also allow for a deeper understanding of the relationship between new firm registration, the regulatory environment, and economic growth. Research using the Entrepreneurship Database has shown a significant relationship between the cost of compliance required to start a business and new firm registration.\n\n### Limitations and exceptions:\nThe definition of entrepreneurship used is limited to the formal sector. Yet, it should be noted that the exclusion of the informal sector is based on the difficulties of quantifying the number of firms that compose it, rather than on its relevance for developing economies. Data collected from economies categorized as offshore financial centers by Eurostat are excluded from the analysis, because registered entities in these countries may not fit the objective of the Entrepreneurship Database aiming at measuring formally registered companies with actual economic activities. The information provided by these economies likely reflects a number of shell companies, defined as companies that are registered for tax purposes but are not economically active. The information on offshore centers is collected and published by the Entrepreneurship Database, but it is not used for trend analysis purposes.\n\n### Other notes:\nFor cross-country comparability, only limited liability corporations that operate in the formal sector are included.", "origins": [ { "producer": "Entrepreneurship Database, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Entrepreneurship Database, World Bank (WB), uri: https://www.worldbank.org/en/programs/entrepreneurship. Indicator IC.BUS.NREG (https://data.worldbank.org/indicator/IC.BUS.NREG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.BUS.NREG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "number", "short_unit": "number", "display": { "shortUnit": "number", "unit": "number" } }, "ic_cus_durs_ex": { "title": "Average time to clear exports through customs (days)", "description_from_producer": "Average number of days to clear direct exports through customs.\n\n### Periodicity:\nAd hoc\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nOpen markets allow firms to expand, raise standards for efficiency on exporters, and enable firms to import low cost supplies. However, trading also forces firms to deal with customs services and trade regulations, obtain export and import licenses, and in some cases, firms also face additional costs due to losses during transport. The Enterprise Surveys collect information on the operational constraints faced by exporters and importers and quantifies the trade activity of firms. Indicators provide a measure of the intensity of foreign trade in the private sector.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data, note: All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org. Indicator IC.CUS.DURS.EX (https://data.worldbank.org/indicator/IC.CUS.DURS.EX). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.CUS.DURS.EX", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "days", "short_unit": "days", "display": { "shortUnit": "days", "unit": "days" } }, "ic_cus_durs_im": { "title": "Average time to clear imports through customs (days)", "description_from_producer": "Average number of days to clear imports from customs in the manufacturing sector.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data , note: All surveys were administered using the Enterprise Surveys methodology as outlined in the Methodology page which can be found from www.enterprisesurveys.org. Indicator IC.CUS.DURS.IM (https://data.worldbank.org/indicator/IC.CUS.DURS.IM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.CUS.DURS.IM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_elc_durs": { "title": "Time to obtain an electrical connection (days)", "description_from_producer": "Average wait, in days, experienced to obtain electrical connection from the day this establishment applied for it to the day it received the service.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA strong infrastructure enhances the competitiveness of an economy and generates a business environment conducive to firm growth and development. Good infrastructure efficiently connects firms to their customers and suppliers, and enables the use of modern production technologies. Conversely, deficiencies in infrastructure create barriers to productive opportunities and increase costs for all firms, from micro enterprises to large multinational corporations.\n\nThe Enterprise Surveys capture the dual challenge of providing a strong infrastructure for electricity, water supply, internet connections, etc., and the development of institutions that effectively provide and maintain public services. These indicators show the extent to which firms are faced with failures in the provision of electricity and the effect of these failures on sales. Inadequate electricity supply can increase costs, disrupt production, and reduce profitability. Additionally, these indicators measure the efficiency of the water supply for the manufacturing sector. Many manufacturing sectors depend on reliable and efficient sources of water for their operations. The indicators can also be used to evaluate the efficiency of infrastructure services by quantifying the delays in obtaining electricity, water, and telephone connections. Service delays impose additional costs on firms and may act as barriers to entry and investment.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.ELC.DURS (https://data.worldbank.org/indicator/IC.ELC.DURS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.ELC.DURS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "days", "short_unit": "days", "display": { "shortUnit": "days", "unit": "days" } }, "ic_elc_outg_zs": { "title": "Firms experiencing electrical outages (% of firms)", "description_from_producer": "Percentage of firms that experienced power outages over the last complete fiscal year.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA strong infrastructure enhances the competitiveness of an economy and generates a business environment conducive to firm growth and development. Good infrastructure efficiently connects firms to their customers and suppliers, and enables the use of modern production technologies. Conversely, deficiencies in infrastructure create barriers to productive opportunities and increase costs for all firms, from micro enterprises to large multinational corporations.\n\nThe Enterprise Surveys capture the dual challenge of providing a strong infrastructure for electricity, water supply, internet connections, etc., and the development of institutions that effectively provide and maintain public services. These indicators show the extent to which firms are faced with failures in the provision of electricity and the effect of these failures on sales. Inadequate electricity supply can increase costs, disrupt production, and reduce profitability. Additionally, these indicators measure the efficiency of the water supply for the manufacturing sector. Many manufacturing sectors depend on reliable and efficient sources of water for their operations. The indicators can also be used to evaluate the efficiency of infrastructure services by quantifying the delays in obtaining electricity, water, and telephone connections. Service delays impose additional costs on firms and may act as barriers to entry and investment.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.ELC.OUTG.ZS (https://data.worldbank.org/indicator/IC.ELC.OUTG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.ELC.OUTG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms" } }, "ic_frm_bkwc_zs": { "title": "Firms using banks to finance working capital (% of firms)", "description_from_producer": "Percentage of firms using bank loans to finance working capital.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators of how firms finance their operations and of the characteristics of their financial transactions. For example, Enterprise Surveys provide indicators that compare the relative use of various sources to finance investment. Excessive reliance on internal funds is a sign of potentially inefficient financial intermediation. Another set of indicators measures the use of financial markets by individual firms. It presents the percentage of working capital that is financed by external sources to the firm, and a measure of the burden imposed by loan requirements measured by collateral levels relative to the value of the loans. Additional indicators focus on the use of financial services by private firms both on the credit side, by measuring the percentage of firms with bank loans or lines or credit, and on the deposit mobilization side, by measuring the percentage of firms with checking or savings accounts.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\n\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.BKWC.ZS (https://data.worldbank.org/indicator/IC.FRM.BKWC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.BKWC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms" } }, "ic_frm_bnkl_zs": { "title": "Firms with a bank loan/line of credit (% of firms)", "description_from_producer": "Percentage of firms that have bank loans or a line of credit.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.BNKL.ZS (https://data.worldbank.org/indicator/IC.FRM.BNKL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.BNKL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_frm_bnks_zs": { "title": "Firms using banks to finance investment (% of firms)", "description_from_producer": "Percentage of firms using banks to finance purchases of fixed assets.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators of how firms finance their operations and of the characteristics of their financial transactions. For example, Enterprise Surveys provide indicators that compare the relative use of various sources to finance investment. Excessive reliance on internal funds is a sign of potentially inefficient financial intermediation. Another set of indicators measures the use of financial markets by individual firms. It presents the percentage of working capital that is financed by external sources to the firm, and a measure of the burden imposed by loan requirements measured by collateral levels relative to the value of the loans. Additional indicators focus on the use of financial services by private firms both on the credit side, by measuring the percentage of firms with bank loans or lines or credit, and on the deposit mobilization side, by measuring the percentage of firms with checking or savings accounts.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\n\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.BNKS.ZS (https://data.worldbank.org/indicator/IC.FRM.BNKS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.BNKS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms" } }, "ic_frm_brib_zs": { "title": "Bribery incidence (% of firms experiencing at least one bribe payment request)", "description_from_producer": "The percent of firms experiencing at least one bribe payment request across 6 public transactions dealing with utilities access, permits, licenses, and taxes.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nCorruption by public officials may present a major administrative and financial burden on firms. Corruption creates an unfavorable business environment by undermining the operational efficiency of firms and raising the costs and risks associated with doing business.\n\nInefficient regulations constrain firm efficiency as they present opportunities for soliciting bribes where firms are required to make \u201cunofficial\u201d payments to public officials to get things done. In many countries bribes are common and quite high and they add to the bureaucratic costs in obtaining required permits and licenses. They can be a serious impediment for firms\u2019 growth and development.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.BRIB.ZS (https://data.worldbank.org/indicator/IC.FRM.BRIB.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.BRIB.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms experiencing at least one bribe payment request", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms experiencing at least one bribe payment request" } }, "ic_frm_cdp_zs": { "title": "Firms that use a third party to resolve commercial disputes (% of firms)", "description_from_producer": "Percentage of firms that use courts, arbitration, mediation, or conciliation to resolve or attempt to resolve their commercial disputes.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.CDP.ZS (https://data.worldbank.org/indicator/IC.FRM.CDP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.CDP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_frm_cmpu_zs": { "title": "Firms competing against unregistered firms (% of firms)", "description_from_producer": "Percentage of firms competing against unregistered or informal firms.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA large informal sector has serious consequences for the formal private sector. The informal sector may pose unfair competition for formal firms and also deprive governments of potential tax revenue and diminish a government's capacity for regulatory oversight. The Enterprise Surveys capture key dimensions the degree of informality in an economy. For example, the set of indicators (unregistered start-ups) shows the percentage of firms that started operation without being formally registered. It approximates the prevalence of informality in the private economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.CMPU.ZS (https://data.worldbank.org/indicator/IC.FRM.CMPU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.CMPU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms" } }, "ic_frm_co2_zs": { "title": "Firms monitoring own CO2 emissions (% of firms)", "description_from_producer": "Percentage of firms tracking their own CO2 emissions over the past three years\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys , World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.CO2.ZS (https://data.worldbank.org/indicator/IC.FRM.CO2.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.CO2.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_frm_corr_zs": { "title": "Informal payments to public officials (% of firms)", "description_from_producer": "Percentage of establishments that consider that firms with characteristics similar to theirs are making informal payments or giving gifts to public officials to \"get things done\u201d with regard to customs, taxes, licenses, regulations, services etc.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nCorruption by public officials may present a major administrative and financial burden on firms. Corruption creates an unfavorable business environment by undermining the operational efficiency of firms and raising the costs and risks associated with doing business.\n\nInefficient regulations constrain firm efficiency as they present opportunities for soliciting bribes where firms are required to make \u201cunofficial\u201d payments to public officials to get things done. In many countries bribes are common and quite high and they add to the bureaucratic costs in obtaining required permits and licenses. They can be a serious impediment for firms\u2019 growth and development.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\n\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.CORR.ZS (https://data.worldbank.org/indicator/IC.FRM.CORR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.CORR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms" } }, "ic_frm_crdc_fl_zs": { "title": "Firms that are fully credit constrained (% of firms)", "description_from_producer": "Firms are categorized as fully credit constrained if they do not have access to external finance, and any of the following two conditions are met: (1) the firm did not apply for a loan for any reason other than the lack of need for it; or (2) the firm applied for a loan but the application was rejected, even when it has access to equity financing.\n\nThis indicator is based on Islam and Rodriguez Meza (2023, Islam, Asif Mohammed and Jorge Luis Rodriguez Meza. \u201cHow Prevalent Are Credit-Constrained Firms in the Formal Private Sector? Evidence Using Global Surveys\u201d. World Bank Policy Research Working Paper; no. WPS 10502).\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys , World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.CRDC.FL.ZS (https://data.worldbank.org/indicator/IC.FRM.CRDC.FL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.CRDC.FL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_frm_crdc_pt_zs": { "title": "Firms that are partially credit constrained (% of firms)", "description_from_producer": "Firms are categorized as partially credit constrained if any of the following conditions are met: (1) the firm applied for a loan and the application was partially approved; (2) the firm applied for a loan and the application was rejected, but the firm has access to external sources of finance excluding any equity finance; or (3) the firm has external finance but did not apply for a loan due to any reason other than no need for it.\n\nThis indicator is based on Islam and Rodriguez Meza (2023, Islam, Asif Mohammed and Jorge Luis Rodriguez Meza. \u201cHow Prevalent Are Credit-Constrained Firms in the Formal Private Sector? Evidence Using Global Surveys\u201d. World Bank Policy Research Working Paper; no. WPS 10502).\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.CRDC.PT.ZS (https://data.worldbank.org/indicator/IC.FRM.CRDC.PT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.CRDC.PT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_frm_durs": { "title": "Time required to obtain an operating license (days)", "description_from_producer": "The average wait, in days, to obtain an operating license, from the day of the application to the day it was granted.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nGood economic governance in areas of regulations, permits and licenses are among the fundamental pillars for the creation of a favorable business environment.\n\nThe Enterprise Surveys provide qualitative and quantitative measures of regulations. For example, the Enterprise Surveys approximates the \u201ctime tax\u201d imposed by regulations: it measures the time spent by senior management in meetings with public officials. Another indicator, the average number of visits or required meetings with tax officials, measures the average number of tax inspections or meetings with tax inspectors in each year.\n\nEffective regulations address market failures that inhibit productive investment and reconcile private and public interests. The number of permits and approvals that businesses need to obtain, and the time it takes to obtain them, are expensive and time consuming. The existing legislation of a country also determines the mix of legal forms private firms take and determines the level of protection for investors thus affecting the incentives to invest. Those indicators focus on the efficiency of business licensing and permit services. The indicators evaluate the delays faced when demanding these services.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.DURS (https://data.worldbank.org/indicator/IC.FRM.DURS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.DURS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "days", "short_unit": "days", "display": { "shortUnit": "days", "unit": "days" } }, "ic_frm_engm_zs": { "title": "Firms adopting energy management measures to reduce emissions (% of firms)", "description_from_producer": "Share of companies adopting energy-saving practices to reduce emissions over the past three years.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.ENGM.ZS (https://data.worldbank.org/indicator/IC.FRM.ENGM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.ENGM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_frm_exs_zs": { "title": "Firms exporting directly at least 10% of sales (% of firms)", "description_from_producer": "Percentage of firms that export directly at least 10% of their total annual sales.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.EXS.ZS (https://data.worldbank.org/indicator/IC.FRM.EXS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.EXS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_frm_femm_zs": { "title": "Firms with female top manager (% of firms)", "description_from_producer": "Percentage of firms with females as the top manager.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators that describe several dimensions of gender composition in the workforce. It also collects information on the characteristics of the workforce employed in the non-agricultural private economy. The set of indicators presents the composition of the firm's workforce by type of contract and gender. Labor regulations have a direct effect on the type of employment favored by firms and they may have a different impact by gender. Other indicators present the composition of the workforce classified into temporary and permanent workers and reflect the participation of women in regular full time employment, along with the firms\u2019 inclusion of women in formal trainings.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.\n\nRelevance to gender indicator: Women are vastly underrepresented in decision making positions at the top level in the private sector and this indicator monitors progress that has been made.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.FEMM.ZS (https://data.worldbank.org/indicator/IC.FRM.FEMM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.FEMM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms" } }, "ic_frm_femo_zs": { "title": "Firms with female participation in ownership (% of firms)", "description_from_producer": "Percentage of firms with females among the owners.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators that describe several dimensions of gender composition in the workforce. It also collects information on the characteristics of the workforce employed in the non-agricultural private economy. The set of indicators presents the composition of the firm's workforce by type of contract and gender. Labor regulations have a direct effect on the type of employment favored by firms and they may have a different impact by gender. Other indicators present the composition of the workforce classified into temporary and permanent workers and reflect the participation of women in regular full time employment, along with the firms\u2019 inclusion of women in formal trainings.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.FEMO.ZS (https://data.worldbank.org/indicator/IC.FRM.FEMO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.FEMO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms" } }, "ic_frm_fo_zs": { "title": "Firms with at least 10% foreign ownership", "description_from_producer": "Percentage of firms that have at least 10% owned by private foreign individuals, companies or organizations.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.FO.ZS (https://data.worldbank.org/indicator/IC.FRM.FO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.FO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_frm_freg_zs": { "title": "Firms formally registered when operations started (% of firms)", "description_from_producer": "Percentage of firms formally registered when they started operations in the country.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA large informal sector has serious consequences for the formal private sector. The informal sector may pose unfair competition for formal firms and also deprive governments of potential tax revenue and diminish a government's capacity for regulatory oversight. The Enterprise Surveys capture key dimensions the degree of informality in an economy. For example, the set of indicators (unregistered start-ups) shows the percentage of firms that started operation without being formally registered. It approximates the prevalence of informality in the private economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.FREG.ZS (https://data.worldbank.org/indicator/IC.FRM.FREG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.FREG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms" } }, "ic_frm_lotm_zs": { "title": "Firms where largest owner is also the top manager (% of firms)", "description_from_producer": "Percent of firms where the largest owner is also the top manager.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.LOTM.ZS (https://data.worldbank.org/indicator/IC.FRM.LOTM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.LOTM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_frm_metg_zs": { "title": "Firms visited or required meetings with tax officials (% of firms)", "description_from_producer": "Percentage of firms that were visited or inspected by tax officials or were required to meet with them over the last year.\n\n### Periodicity:\nAd hoc\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.METG.ZS (https://data.worldbank.org/indicator/IC.FRM.METG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.METG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms" } }, "ic_frm_nprd_zs": { "title": "Firms that introduced a new product/service and process, and spent on R&D", "description_from_producer": "Percentage of medium and large firms that introduced a new product/service and process over last 3 years, and spent on R&D over last fiscal year (excluding small firms).\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.NPRD.ZS (https://data.worldbank.org/indicator/IC.FRM.NPRD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.NPRD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_frm_outg_zs": { "title": "Value lost due to electrical outages (% of sales for affected firms)", "description_from_producer": "Losses due to electrical outages, as percentage of total annual sales. The value represents average losses for all firms which reported outages (please see indicator IC.ELC.OUTG.ZS).\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nA strong infrastructure enhances the competitiveness of an economy and generates a business environment conducive to firm growth and development. Good infrastructure efficiently connects firms to their customers and suppliers, and enables the use of modern production technologies. Conversely, deficiencies in infrastructure create barriers to productive opportunities and increase costs for all firms, from micro enterprises to large multinational corporations.\n\nThe Enterprise Surveys capture the dual challenge of providing a strong infrastructure for electricity, water supply, internet connections, etc., and the development of institutions that effectively provide and maintain public services. These indicators show the extent to which firms are faced with failures in the provision of electricity and the effect of these failures on sales. Inadequate electricity supply can increase costs, disrupt production, and reduce profitability. Additionally, these indicators measure the efficiency of the water supply for the manufacturing sector. Many manufacturing sectors depend on reliable and efficient sources of water for their operations. The indicators can also be used to evaluate the efficiency of infrastructure services by quantifying the delays in obtaining electricity, water, and telephone connections. Service delays impose additional costs on firms and may act as barriers to entry and investment.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.OUTG.ZS (https://data.worldbank.org/indicator/IC.FRM.OUTG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.OUTG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of sales for affected firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of sales for affected firms" } }, "ic_frm_taxe_zs": { "title": "Firms filling taxes electronically (% of firms)", "description_from_producer": "Percentage of firms filing taxes electronically either fully or partially.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.TAXE.ZS (https://data.worldbank.org/indicator/IC.FRM.TAXE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.TAXE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_frm_trng_zs": { "title": "Firms offering formal training (% of firms)", "description_from_producer": "Percentage of firms offering formal training programs for its permanent, full-time employees.\n\n### Periodicity:\nAd hoc\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nThe Enterprise Surveys provide indicators that describe information on the characteristics of the workforce employed in the non-agricultural private economy. The set of indicators presents the composition of the firm's workforce by type of contract and gender, the composition of the workforce classified into temporary and permanent workers, and reflects the participation of women in regular full-time employment. Labor regulations have a direct effect on the type of employment favored by firms and they may have a different impact by gender.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.FRM.TRNG.ZS (https://data.worldbank.org/indicator/IC.FRM.TRNG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.FRM.TRNG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms" } }, "ic_gov_durs_zs": { "title": "Time spent dealing with the requirements of government regulations (% of senior management time)", "description_from_producer": "Average percentage of senior management\u2019s time that is spent in a typical week dealing with requirements imposed by government regulations (eg. Taxes, customs, labor regulations, licensing and registration), including dealings with officials, completing forms, et cetera.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nGood economic governance in areas of regulations, permits and licenses are among the fundamental pillars for the creation of a favorable business environment.\n\nThe Enterprise Surveys provide qualitative and quantitative measures of regulations. For example, the Enterprise Surveys approximates the \u201ctime tax\u201d imposed by regulations: it measures the time spent by senior management in meetings with public officials. Another indicator, the average number of visits or required meetings with tax officials, measures the average number of tax inspections or meetings with tax inspectors in each year.\n\nEffective regulations address market failures that inhibit productive investment and reconcile private and public interests. The number of permits and approvals that businesses need to obtain, and the time it takes to obtain them, are expensive and time consuming. The existing legislation of a country also determines the mix of legal forms private firms take and determines the level of protection for investors thus affecting the incentives to invest. Those indicators focus on the efficiency of business licensing and permit services. The indicators evaluate the delays faced when demanding these services.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.GOV.DURS.ZS (https://data.worldbank.org/indicator/IC.GOV.DURS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.GOV.DURS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of senior management time", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of senior management time" } }, "ic_mng_ind": { "title": "Management practices index", "description_from_producer": "Average management practices index for medium and large firms: composite index that combines information from eight management practices indicators included in the Enterprise Surveys.\n\nThe Enterprise Surveys provide indicators that describe several dimensions of management practices. These indicators measure the extent to which firms implement better practices such as taking long-term actions to fix and avoid problems in production or service-delivery; number, time-horizon, and other features of production of service-provision targets; use of bonuses or promotion to reward better performance, and demotion to limit under-performance.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank Group (WBG), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.MNG.IND (https://data.worldbank.org/indicator/IC.MNG.IND). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.MNG.IND", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ic_tax_gift_zs": { "title": "Firms expected to give gifts in meetings with tax officials (% of firms)", "description_from_producer": "Percentage of firms expected to give gifts or informal payments during meetings with tax officials.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nCorruption by public officials may present a major administrative and financial burden on firms. Corruption creates an unfavorable business environment by undermining the operational efficiency of firms and raising the costs and risks associated with doing business.\n\nInefficient regulations constrain firm efficiency as they present opportunities for soliciting bribes where firms are required to make \u201cunofficial\u201d payments to public officials to get things done. In many countries bribes are common and quite high and they add to the bureaucratic costs in obtaining required permits and licenses. They can be a serious impediment for firms\u2019 growth and development.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.TAX.GIFT.ZS (https://data.worldbank.org/indicator/IC.TAX.GIFT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.TAX.GIFT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of firms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of firms" } }, "ic_tax_metg": { "title": "Number of visits or required meetings with tax officials (average for affected firms)", "description_from_producer": "Average number of visits or required meetings with tax officials.\n\n### Periodicity:\nAd hoc\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.\n\nSince 2005-2006, the WBES implemented by the Enterprise Analysis Unit follow the Global Methodology. Earlier datasets from differing survey instruments have been matched to a standard instrument for dissemination on the website and data portal. Note that data users should exercise caution when comparing raw data and point estimates between surveys that did and surveys that did not adhere to the WBES Global Methodology.\n\nPrivate contractors conduct the WBES on behalf of the World Bank. Surveys are usually carried out in cooperation with business organizations and government agencies promoting job creation and economic growth, but anonymity is never compromised. The mode of data collection is face-to-face interviews (in-person or virtual).\n\nThe WBES is answered by business owners and top managers. The survey respondents might sometimes involve accountants and human resource managers to answer some questions.\n\u2022 Firms classified with ISIC Rev. 4 codes 10-33, 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95. These are firms in manufacturing, construction, retail, wholesale, hotels, restaurants, transport, storage, communications, professional services, and IT.*\n\u2022 Formal (registered) companies with five or more workers.\n\u2022 Firms that have at least 1% of private ownership.\n\nOccasionally, for a few economies, other sectors are included, such as education or health.\n\n(Starting from August 2022, the WBES uses ISIC Rev. 4 (instead of Rev.3.1) for categorizing business activity. Henceforth, businesses under Section M of ISIC Rev. 4, i.e., professional, scientific, and technical activities, became eligible to participate.)\n\nThe standard WBES questionnaire covers over 15 topics, including firm characteristics, gender participation, access to finance, annual sales, among others. See the questionnaire here.\nOver 90% of the questions are fact-based, while the remaining 10% assess the respondents\u2019 opinions on obstacles to their firm\u2019s growth and performance.\n\nThe WBES uses stratified random sampling, with the following strata:\n\u2022 firm size, most frequently: small (5-19 workers), medium (20-99), and large (100+)\n\u2022 business sector\n\u2022 geographic region within an economy\n\nIdeally, the survey sample frame is derived from the universe of eligible firms obtained from the economy\u2019s statistical office. Sometimes the master list of firms is obtained from other government agencies such as tax or business licensing authorities. In some cases, the list of firms is obtained from business associations or marketing databases. In a few cases, the sample frame is created via block enumeration, where the World Bank \u201cmanually\u201d constructs a list of eligible firms after 1) partitioning an economy\u2019s cities of major economic activity into clusters and blocks, and 2) randomly selecting a subset of blocks which will then be enumerated. Since 2005-06, the publicly available survey documentation details the source of the sample frame.\n\n### Development relevance:\nThe Enterprise Surveys focus on the many factors that shape the business environment. These factors can be accommodating or constraining for firms and play an important role in whether a country will prosper or not. An accommodating business environment is one that encourages firms to operate efficiently. Such conditions strengthen incentives for firms to innovate and to increase productivity\u2014key factors for sustainable development. A more productive private sector, in turn, expands employment and contributes taxes necessary for public investment in health, education, and other services. In contrast, a poor business environment increases the obstacles to conducting business activities and decreases a country\u2019s prospects for reaching its potential in terms of employment, production, and welfare.\n\nTaxes play a crucial role in private sector development by providing the necessary public funds to build infrastructure, maintain social services, and create a stable economic environment. Effective tax administration reduces evasion, ensures fairness, and fosters a conducive business climate, thereby encouraging investment and growth in the private sector.\n\nThe Enterprise Surveys provide quantitative and qualitative measures of taxation and its administration. For example, the Enterprise Surveys collects information about the visits from tax officials or requirements to meet them, along with the burden of tax compliance and filing. Furthermore, the Enterprise Surveys provides information about firms\u2019 perception about the taxes and their administration in their respective economy.\n\n### Other notes:\nThe WBES also provides panel data, i.e., interviews with the same firms across multiple years. When conducting a new WBES, maximal effort is expended to re-interview at least half of the firms from the prior WBES. For these panel firms, sampling weights are adjusted to consider the resulting altered probabilities of inclusion in the sample frame.\nStatistical concept(s): The sampling methodology of the World Bank\u2019s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the business environment of individual economies across the world and across groups of firms within each economy; second, to conduct firm performance analyses focusing on how the business environment affect firm-level outcomes such as productivity, job creation, investment, and growth.\n\nTo achieve both objectives the sampling methodology: (a) generates a sample representative of the non-agricultural, non-extractive formal private economy, (b) generates large enough sample sizes for selected industries and other groups of firms to conduct statistically robust analyses with a minimum 7.5% precision for 90% confidence intervals of: (i) Estimates of population proportions (percentages); and (ii) estimates of the mean of log of sales.\n\nThe Universe of Inference of the Enterprise Surveys includes the following list, following ISIC, revision 3.1: all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Following ISIC revision 4 the Universe includes: sections C, F, G, H, I, and divisions 61 and 62 of section J.2 Additionally, the Universe of Inference includes all establishments with five or more employees, fully or partially privately owned: one hundred percent government-owned firms, cooperatives, and firms with less than five employees are excluded.\n\nEnterprise Surveys are stratified by sector of activity, firm size, and geographical location. Stratification by firm size divides the population of firms into 3 strata: small firms (5-19 employees), medium-size firms (20-99 employees), and large firms (100 or more employees); in very large economies a fourth size stratum is added, the top 1% of firms by size. Geographical stratification is defined to reflect the distribution of the non-agricultural economic activity of each country, which in most cases implies covering the main urban centers of the country. Around the world most of the non-agricultural, non-mining economic activity, the ES Universe, is clustered around the main centers of population.\n\nStratification by sector of activity depends on the size of the economy as measured by the Gross National Income (GNI). Very small economies (below $20 billion GNI of 2016) are stratified into 2 groups: manufacturing and services with 75 interviews allocated to each group. For small economies, GNI between $20 billion and $30 billion, the universe is stratified into manufacturing, retail, and the rest services. Medium-size economies, GNI between $30 and 100 billion, single-out the 2 most important manufacturing industries and the remaining manufacturing industries are grouped together into a residual stratum, \u201crest of manufacturing\u201d; retail and \u201crest of services\u201d provide the final two strata. For large and very large economies, further manufacturing and services subsectors are single-out for stratification preserving the residual categories to complete exhaust the universe. Also, given the size of these economies the minimum sample size per level of stratification of 120 can be augmented to account for potential non-response to financial variables that are key for the computation of productivity.", "origins": [ { "producer": "Enterprise Surveys - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Enterprise Surveys, World Bank (WB), uri: https://www.enterprisesurveys.org/en/data. Indicator IC.TAX.METG (https://data.worldbank.org/indicator/IC.TAX.METG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IC.TAX.METG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "average for affected firms", "display": { "unit": "average for affected firms" } }, "ie_ppi_engy_cd": { "title": "Investment in energy with private participation (current US$)", "description_from_producer": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in energy projects with private participation refers to commitments to infrastructure projects in energy (electricity and natural gas: generation, transmission and distribution) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit's databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial's Project Finance International, Euromoney's Project Finance, Media Analytics' Global Water Intelligence, Pinsent Masons' Water Yearbooks, and Platt's Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\n\nStatistical concept(s): PPPs is defined as \u201cany contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.\u201d\nThe term infrastructure refers to:\n\u2022 Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n\u2022 Information and communications technology (ICT): ICT backbone infrastructure\n\u2022 Transport: Airports, railways, ports, and roads.\n\u2022 Water: potable water treatment and distribution, and sewerage collection and treatment.\n\n### Development relevance:\nInvestment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure.\n\n### Limitations and exceptions:\nThe data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nInvestment commitments are the sum of investments in physical assets and payments to the government. Investments in physical assets are resources the project company commits to invest during the contract period in new facilities or in expansion and modernization of existing facilities. Payments to the government are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported.", "origins": [ { "producer": "Private Participation in Infrastructure Project Database, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata. Indicator IE.PPI.ENGY.CD (https://data.worldbank.org/indicator/IE.PPI.ENGY.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IE.PPI.ENGY.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ie_ppi_icti_cd": { "title": "Investment in ICT with private participation (current US$)", "description_from_producer": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in ICT projects with private participation refers to commitments to infrastructure projects in ICT (including land based and submarine cables except purely private telecoms. Instead, it will track ICT backbone infrastructure (fiber optic cables etc) that has an active government component) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit's databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial's Project Finance International, Euromoney's Project Finance, Media Analytics' Global Water Intelligence, Pisent Masons' Water Yearbooks, and Platt's Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template to ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\n\nStatistical concept(s): PPPs is defined as \u201cany contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.\u201d\nThe term infrastructure refers to:\n\u2022 Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n\u2022 Information and communications technology (ICT): ICT backbone infrastructure\n\u2022 Transport: Airports, railways, ports, and roads.\n\u2022 Water: potable water treatment and distribution, and sewerage collection and treatment.\n\n### Development relevance:\nInvestment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure.\n\n### Limitations and exceptions:\nThe data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrum. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported. Data are available 2015 onwards only for ICT.", "origins": [ { "producer": "Private Participation in Infrastructure Project Database, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: http://ppi.worldbank.org. Indicator IE.PPI.ICTI.CD (https://data.worldbank.org/indicator/IE.PPI.ICTI.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IE.PPI.ICTI.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ie_ppi_tran_cd": { "title": "Investment in transport with private participation (current US$)", "description_from_producer": "The Private Participation In Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in transport projects with private participation refers to commitments to infrastructure projects in transport [(a) airport runways and terminals, (b) railways (including fixed assets, freight, intercity passenger, and local passenger); (c) toll roads, bridges, highways, and tunnels, (d) port infrastructure, superstructures, terminals, and channels)] that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit's databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial's Project Finance International, Euromoney's Project Finance, Media Analytics' Global Water Intelligence, Pinsent Masons' Water Yearbooks, and Platt's Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their Web sites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\n\nStatistical concept(s): PPPs is defined as \u201cany contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.\u201d\nThe term infrastructure refers to:\n\u2022 Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n\u2022 Information and communications technology (ICT): ICT backbone infrastructure\n\u2022 Transport: Airports, railways, ports, and roads.\n\u2022 Water: potable water treatment and distribution, and sewerage collection and treatment.\n\n### Development relevance:\nInvestment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure.\n\n### Limitations and exceptions:\nThe data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported.", "origins": [ { "producer": "Private Participation in Infrastructure Project Database, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata. Indicator IE.PPI.TRAN.CD (https://data.worldbank.org/indicator/IE.PPI.TRAN.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IE.PPI.TRAN.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ie_ppi_watr_cd": { "title": "Investment in water and sanitation with private participation (current US$)", "description_from_producer": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Investment in water projects with private participation refers to commitments to infrastructure projects in potable water (treatment and distribution, and sewerage collection and treatment that has an active government component) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: A team of researchers gather data for each of the regions using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit's databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial's Project Finance International, Euromoney's Project Finance, Media Analytics' Global Water Intelligence, Pinsent Masons' Water Yearbooks, and Platt's Power in Asia, etc.), specialist portal (such as Privatization, IPAnet, and Privatization Barometer), Internet resources (such as web sites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their Websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template to make sure data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then for the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as \u201cany contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.\u201d\nThe term infrastructure refers to:\n\u2022 Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n\u2022 Information and communications technology (ICT): ICT backbone infrastructure\n\u2022 Transport: Airports, railways, ports, and roads.\n\u2022 Water: potable water treatment and distribution, and sewerage collection and treatment.\n\n### Development relevance:\nInvestment in infrastructure projects with private participation has made important contributions to easing fiscal constraints, improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, pioneering better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure.\n\n### Limitations and exceptions:\nThe data on investment in infrastructure projects with private participation refer to all investment (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported.", "origins": [ { "producer": "Private Participation in Infrastructure Project Database, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata. Indicator IE.PPI.WATR.CD (https://data.worldbank.org/indicator/IE.PPI.WATR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IE.PPI.WATR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ie_ppn_engy_cd": { "title": "Public private partnerships investment in energy (current US$)", "description_from_producer": "The Private Participation In Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in energy refers to commitments to infrastructure projects in energy (electricity and natural gas: generation, transmission, and distribution) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). Divestitures and merchant projects are excluded. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit's databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial's Project Finance International, Euromoney's Project Finance, Media Analytics' Global Water Intelligence, Pinsent Masons' Water Yearbooks, and Platt's Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as \u201cany contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.\u201d\nThe term infrastructure refers to:\n\u2022 Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n\u2022 Information and communications technology (ICT): ICT backbone infrastructure\n\u2022 Transport: Airports, railways, ports, and roads.\n\u2022 Water: potable water treatment and distribution, and sewerage collection and treatment.\n\n### Development relevance:\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\n### Limitations and exceptions:\nThe data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported.", "origins": [ { "producer": "Private Participation in Infrastructure Project Database, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata. Indicator IE.PPN.ENGY.CD (https://data.worldbank.org/indicator/IE.PPN.ENGY.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IE.PPN.ENGY.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ie_ppn_icti_cd": { "title": "Public private partnerships investment in ICT (current US$)", "description_from_producer": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in ICT refers to commitments to projects in ICT backbone infrastructure (including land based and submarine cables) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). It excludes divestitures and merchant projects. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars and available 2015 onwards.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit's databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial's Project Finance International, Euromoney's Project Finance, Media Analytics' Global Water Intelligence, Pinsent Masons' Water Yearbooks, and Platt's Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\nStatistical concept(s): PPPs is defined as \u201cany contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.\u201d\nThe term infrastructure refers to:\n\u2022 Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n\u2022 Information and communications technology (ICT): ICT backbone infrastructure\n\u2022 Transport: Airports, railways, ports, and roads.\n\u2022 Water: potable water treatment and distribution, and sewerage collection and treatment.\n\n### Development relevance:\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\n### Limitations and exceptions:\nThe data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrum. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported.", "origins": [ { "producer": "Private Participation in Infrastructure Project Database, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata. Indicator IE.PPN.ICTI.CD (https://data.worldbank.org/indicator/IE.PPN.ICTI.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IE.PPN.ICTI.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ie_ppn_tran_cd": { "title": "Public private partnerships investment in transport (current US$)", "description_from_producer": "The Private Participation In Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in transport refers to commitments to infrastructure projects in transport [(a) airport runways and terminals, (b) railways (including fixed assets, freight, intercity passenger, and local passenger); (c) toll roads, bridges, highways, and tunnels, (d) port infrastructure, superstructures, terminals, and channels)] that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). Divestitures and merchant projects are excluded. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit's databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial's Project Finance International, Euromoney's Project Finance, Media Analytics' Global Water Intelligence, Pinsent Masons' Water Yearbooks, and Platt's Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as \u201cany contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.\u201d\nThe term infrastructure refers to:\n\u2022 Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n\u2022 Information and communications technology (ICT): ICT backbone infrastructure\n\u2022 Transport: Airports, railways, ports, and roads.\n\u2022 Water: potable water treatment and distribution, and sewerage collection and treatment.\n\n### Development relevance:\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\n### Limitations and exceptions:\nThe data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported.", "origins": [ { "producer": "Private Participation in Infrastructure Project Database, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata. Indicator IE.PPN.TRAN.CD (https://data.worldbank.org/indicator/IE.PPN.TRAN.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IE.PPN.TRAN.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ie_ppn_watr_cd": { "title": "Public private partnerships investment in water and sanitation (current US$)", "description_from_producer": "The Private Participation in Infrastructure (PPI) Database records contractual arrangements for public infrastructure projects in low- and middle-income countries (as classified by the World Bank) that have reached financial closure, in which private parties assume operating risk. Public private partnerships in water refers to commitments to infrastructure projects in potable water (treatment and distribution, and sewerage collection and treatment that has an active government component) that have reached financial closure and directly or indirectly serve the public. The types of projects included are management and lease contracts, brownfield projects, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). It excludes divestitures and merchant projects. Investments are classified as one of two types: (1) Investments in physical assets: resources the project company commits to invest in expanding and modernizing facilities and (2) Payments to the government: to acquire state-owned enterprises or rights to provide services in a specific area or to use radio spectrum. Data is presented based on investment year. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: A team of researchers gather data using public sources (from government and MDBs websites); commercial news databases (such as Factiva, Business News America, ISI Emerging markets, and the Economist Intelligence Unit\u2019s databases) as well as from commercial specialized and industry publications/subscriptions (Thomson Financial\u2019s Project Finance International, Euromoney\u2019s Project Finance, Media Analytics\u2019 Global Water Intelligence, Pisent Masons\u2019 Water Yearbooks, and Platt\u2019s Power in Asia, etc.), specialist portals (such as Privatization, IPAnet, and Privatization Barometer), online resources (such as websites of project companies, privatization or Public-Private Partnership (PPP) agencies, and regulatory agencies) sponsor information (primarily through their websites, annual reports, press releases, and financial reports such as 10K and 20F forms submitted to the NYSE) and multilateral development agencies primarily through information on their websites, annual reports, and other studies.\n\nData is uploaded to an administrative website through a template ensure that the data is standardized. Data is validated by a group of experts in Singapore first (PPI team), then by the World Bank focal points colleagues.\n\nData is later uploaded to the public website (www.ppi.worldbank.org) and made available free of charge. The website has a mechanism for challenges to the data and welcomes all PPP units to give feedback about any project.\n\nStatistical concept(s): PPPs is defined as \u201cany contractual arrangement between a public entity or authority and a private entity, for providing a public asset or service, in which the private party bears significant risk and management responsibility.\u201d\nThe term infrastructure refers to:\n\u2022 Energy: electricity generation, transmission, and distribution, and natural gas transmission and\ndistribution pipelines\n\u2022 Information and communications technology (ICT): ICT backbone infrastructure\n\u2022 Transport: Airports, railways, ports, and roads.\n\u2022 Water: potable water treatment and distribution, and sewerage collection and treatment.\n\n### Development relevance:\nPrivate sector development and investment - tapping private sector initiative and investment for socially useful purposes - are critical for poverty reduction. In parallel with public sector efforts, private investment, especially in competitive markets, has tremendous potential to contribute to growth. Private markets are the engine of productivity growth, creating productive jobs and higher incomes. And with government playing a complementary role of regulation, funding, and service provision, private initiative and investment can help provide the basic services and conditions that empower poor people - by improving health, education, and infrastructure. Investment in infrastructure projects with private participation has made important contributions to improving the efficiency of infrastructure services, and extending delivery to poor people. Developing countries have been in the forefront, looking for better approaches to infrastructure services and reaping the benefits of greater competition and customer focus. Entrepreneurship is essential to the dynamism of the modern market economy, and a greater entry density of new businesses can foster competition and economic growth.\n\n### Limitations and exceptions:\nThe data on investment in infrastructure projects with private participation refer to all investment commitments (public and private) in projects in which a private company assumes operating risk during the operating period or development and operating risk during the contract period. Investment refers to commitments not disbursements. Foreign state-owned companies are considered private entities for the purposes of this measure.\n\nMovable assets and small projects are excluded. The types of projects included are operations and management contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data on the projects are compiled from publicly available information. The database aims to be as comprehensive as possible, but some projects - particularly those involving local and small-scale operators - may be omitted because they are not publicly reported.", "origins": [ { "producer": "Private Participation in Infrastructure Project Database, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Private Participation in Infrastructure Project Database, World Bank (WB), uri: https://ppi.worldbank.org/en/ppidata. Indicator IE.PPN.WATR.CD (https://data.worldbank.org/indicator/IE.PPN.WATR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IE.PPN.WATR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ip_ids_nrct": { "title": "Industrial design applications, nonresident, by count", "description_from_producer": "Industrial design applications are applications to register an industrial design with a national or regional Intellectual Property (IP) offices and designations received by relevant offices through the Hague System. Non-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled. Design count is used to render application data for industrial applications across offices comparable, as some offices follow a single-class/single-design filing system while other have a multiple class/design filing system.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\n\nStatistical concept(s): Industrial designs are applied to a wide variety of industrial products and handicrafts. They refer to the ornamental or aesthetic aspects of a useful article, including compositions of lines or colors or any three-dimensional forms that give a special appearance to a product or handicraft. The holder of a registered industrial design has exclusive rights against unauthorized copying or imitation of the design by third parties. Industrial design registrations are valid for a limited period. The term of protection is usually 15 years in most jurisdictions. However, differences in legislation exist, notably in China (which provides for a 10-year term from the application date).\n\nNon-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled.\n\nDesign count: The number of designs contained in an industrial design application or registration. Under the Hague System for the International Registration of Industrial Designs, it is possible for an applicant to obtain protection for up to 100 industrial designs for products belonging to one and the same class by filing a single application. Some national or regional IP offices allow applications to contain more than one design for the same product or within the same class, while others allow only one design per application. In order to capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration design counts.\n\n### Development relevance:\nThe progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself.\n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used.\n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone \u2013 including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries \u2013 which bring pleasure to millions of people worldwide \u2013 would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)\n\n### Limitations and exceptions:\nData are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods.", "origins": [ { "producer": "World Intellectual Property Organization (WIPO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. Indicator IP.IDS.NRCT (https://data.worldbank.org/indicator/IP.IDS.NRCT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IP.IDS.NRCT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ip_ids_rsct": { "title": "Industrial design applications, resident, by count", "description_from_producer": "Industrial design applications are applications to register an industrial design with a national or regional Intellectual Property (IP) offices and designations received by relevant offices through the Hague System. A resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. Design count is used to render application data for industrial applications across offices comparable, as some offices follow a single-class/single-design filing system while others have a multiple class/design filing system.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\nStatistical concept(s): Industrial designs are applied to a wide variety of industrial products and handicrafts. They refer to the ornamental or aesthetic aspects of a useful article, including compositions of lines or colors or any three-dimensional forms that give a special appearance to a product or handicraft. The holder of a registered industrial design has exclusive rights against unauthorized copying or imitation of the design by third parties. Industrial design registrations are valid for a limited period. The term of protection is usually 15 years in most jurisdictions. However, differences in legislation exist, notably in China (which provides for a 10-year term from the application date).\n\nFor statistical purposes, a resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. For example, an application filed with the Japan Patent Office (JPO) by a resident of Japan is considered a resident application from the perspective of the JPO. Resident applications are sometimes referred to as \u201cdomestic applications.\u201d A resident grant/registration is an IP right issued on the basis of a resident application.\n\nDesign count: The number of designs contained in an industrial design application or registration. Under the Hague System for the International Registration of Industrial Designs, it is possible for an applicant to obtain protection for up to 100 industrial designs for products belonging to one and the same class by filing a single application. Some national or regional IP offices allow applications to contain more than one design for the same product or within the same class, while others allow only one design per application. In order to capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration design counts.\n\n### Development relevance:\nThe progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself.\n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used.\n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone \u2013 including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries \u2013 which bring pleasure to millions of people worldwide \u2013 would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)\n\n### Limitations and exceptions:\nData are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods.", "origins": [ { "producer": "World Intellectual Property Organization (WIPO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. Indicator IP.IDS.RSCT (https://data.worldbank.org/indicator/IP.IDS.RSCT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IP.IDS.RSCT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ip_jrn_artc_sc": { "title": "Scientific and technical journal articles", "description_from_producer": "Article counts refer to publications from a selection of conference proceedings and peer-reviewed journals from Scopus in science and engineering fields, according to the National Center for Science and Engineering Statistics Taxonomy of Disciplines.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: The Science and Engineering Indicators 2024 report \u201cPublications Output: U.S. Trends and International Comparisons\u201d uses a large database of publication records as a source of bibliometric data. Bibliometric data include each article\u2019s title, author(s), authors\u2019 institution(s), references, journal title, unique article-identifying information (journal volume, issue, and page numbers or digital object identifier), and year or date of publication. The PBS report uses Scopus, a bibliometric database owned by Elsevier and containing scientific literature with English titles and abstracts, to examine national and global scientific publication\u2013related activity.?\n\nArticle counts refer to publications from a selection of conference proceedings and peer-reviewed journals in science and engineering fields from Scopus, according to the National Center for Science and Engineering Statistics Taxonomy of Disciplines: agricultural sciences, astronomy and astrophysics, biological and biomedical sciences, chemistry, computer and information sciences; engineering; geosciences, atmospheric sciences, and ocean sciences; health sciences; material sciences; mathematics and statistics; natural resources and conservation; physics; psychology; social sciences.\n\n\n\nStatistical concept(s): The number of journal articles is presented using fractional counting: a method of counting science and engineering publications in which credit for coauthored publications is divided among the collaborating institutions or regions, countries, or economies based on the proportion of their participating authors. Fractional counting allocates the publication count based on the proportion of the coauthors named on the article with institutional addresses from each region, country, or economy. Fractional counting enables the counts to sum up to the number of total articles. (Source: https://ncses.nsf.gov/pubs/nsb202333/glossary)\n\n### Development relevance:\nA scientific journal is a periodical publication intended to further the progress of science, usually by reporting new research. Most journals are highly specialized, although some of the oldest journals such as Nature publish articles and scientific papers across a wide range of scientific fields. Scientific journals contain articles that have been peer reviewed. When a scientific journal describes experiments or calculations, they must supply enough details that an independent researcher could repeat the experiment or calculation to verify the results. Each such journal article becomes part of the permanent scientific record.\n\nSome journals, such as Nature, Science, Proceedings of the National Academy of Sciences of the United States of America (PNAS), and Physical Review Letters, have a reputation of publishing articles that mark a fundamental breakthrough in their respective fields.\n\n### Limitations and exceptions:\nBecause the bibliometric database is constantly updated, the National Center for Science and Engineering Statistics (NCSES) does not recommend comparing bibliometric data across different editions of the Science and Engineering Indicators publication. For each edition of Indicators, NCSES uses a fixed snapshot of the database. This means that although trends are comparable, the exact number of articles, citations, and other data will vary across editions. For more information about comparing fixed versus dynamic journal data sets, see Schneider et al. (2019). Data before 2003 is sourced from earlier editions of the Science and Engineering Indicators report and may not be strictly comparable with 2003-2022 data.\n\nThe Scopus database is constructed from articles and conference proceedings with an English-language title and abstract; therefore, the database contains an unmeasurable bias because not all science and engineering (S&E) articles and conference proceedings meet the English language requirement (Elsevier 2020). (Source: https://ncses.nsf.gov/pubs/nsb202333/technical-appendix)", "origins": [ { "producer": "National Science Foundation Science and Engineering Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Science and Engineering Indicators, National Science Foundation (NSF), uri: https://ncses.nsf.gov/indicators. Indicator IP.JRN.ARTC.SC (https://data.worldbank.org/indicator/IP.JRN.ARTC.SC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IP.JRN.ARTC.SC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ip_pat_nres": { "title": "Patent applications, nonresidents", "description_from_producer": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention--a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Non-resident patent applications are from applicants outside the relevant State or region. Patent data cover applications and grants classified by field of technology. International applications series distinguish four subcategories: a) patents taken out by residents of a country in that country; b) patents taken out in a country by non-residents of that country; c) total patents registered in the country or naming it; d) patents taken out outside a country by its residents. Data on patents granted only distinguish between patents awarded to residents and to non-residents. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\nPatent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention - a product or process that provides a new way of doing something or offers a new technical solution to a problem.\n\n### Development relevance:\nThe Patent Cooperation Treaty (www.wipo.int/pct) provides a two phase system for filing patent. International applications under the treaty provide for a national patent grant only - there is no international patent. The national filing represents the applicant's seeking of patent protection for a given territory, whereas international filings, while representing a legal right, do not accurately reflect where patent protection is sought. Resident filings are those from residents of the country concerned. Nonresident filings are from applicants abroad. For regional offices applications from residents of any member state of the regional patent convention are considered nonresident filings. Some offices (notably the U.S. Patent and Trademark Office) use the residence of the inventor rather than the applicant to classify filings.\n\nPatent data are a great resource for the study of technical change in a country or region. Patent data provide a uniquely detailed source of information on inventive activity and the multiple dimensions of the inventive process (e.g. geographical location, technical and institutional origin, individuals and networks). Furthermore, patent data form a consistent basis for comparisons across time and across countries.\n\nPatent data can be used in the analysis of a wide array of topics related to technical change and patenting activity including industry-science linkages, patenting strategies by companies, internationalization of research, and indicators on the value of patents. Patent-based statistics reflect the inventive performance of countries, regions and firms, as well as other aspects of the dynamics of the innovation process such as co-operation in innovation or technology paths.\n\n### Limitations and exceptions:\nA patent is an exclusive right granted for a specified period (generally 20 years) for a new way of doing something or a new technical solution to a problem - an invention. The invention must be of practical use and display a characteristic unknown in the existing body of knowledge in its field. Most countries have systems to protect patentable inventions.\n\nUnless otherwise stated, statistics on the number of resident and non-resident patent applications include those filed via the PCT system as PCT national/regional phase entries.", "origins": [ { "producer": "World Intellectual Property Organization (WIPO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WIPO Patent Report: Statistics on Worldwide Patent Activity, World Intellectual Property Organization (WIPO), note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. Indicator IP.PAT.NRES (https://data.worldbank.org/indicator/IP.PAT.NRES). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IP.PAT.NRES", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ip_pat_resd": { "title": "Patent applications, residents", "description_from_producer": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention--a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Resident patent applications are those for which the first-named applicant or assignee is a resident of the State or region concerned. In the case of regional offices such as the European Patent Office, a resident is an applicant from any of the member States of the regional patent convention.\n\nPatent data cover applications and grants classified by field of technology. International applications series distinguish four subcategories: a) patents taken out by residents of a country in that country; b) patents taken out in a country by non-residents of that country; c) total patents registered in the country or naming it; d) patents taken out outside a country by its residents. Data on patents granted only distinguish between patents awarded to residents and to non-residents. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\nPatent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention - a product or process that provides a new way of doing something or offers a new technical solution to a problem.\n\n### Development relevance:\nThe Patent Cooperation Treaty (www.wipo.int/pct) provides a two phase system for filing patent. International applications under the treaty provide for a national patent grant only - there is no international patent. The national filing represents the applicant's seeking of patent protection for a given territory, whereas international filings, while representing a legal right, do not accurately reflect where patent protection is sought. Resident filings are those from residents of the country concerned. Nonresident filings are from applicants abroad. For regional offices applications from residents of any member state of the regional patent convention are considered nonresident filings. Some offices (notably the U.S. Patent and Trademark Office) use the residence of the inventor rather than the applicant to classify filings.\n\nPatent data are a great resource for the study of technical change in a country or region. Patent data provide a uniquely detailed source of information on inventive activity and the multiple dimensions of the inventive process (e.g. geographical location, technical and institutional origin, individuals and networks). Furthermore, patent data form a consistent basis for comparisons across time and across countries.\n\nPatent data can be used in the analysis of a wide array of topics related to technical change and patenting activity including industry-science linkages, patenting strategies by companies, internationalization of research, and indicators on the value of patents. Patent-based statistics reflect the inventive performance of countries, regions and firms, as well as other aspects of the dynamics of the innovation process such as co-operation in innovation or technology paths.\n\n### Limitations and exceptions:\nA patent is an exclusive right granted for a specified period (generally 20 years) for a new way of doing something or a new technical solution to a problem - an invention. The invention must be of practical use and display a characteristic unknown in the existing body of knowledge in its field. Most countries have systems to protect patentable inventions.", "origins": [ { "producer": "World Intellectual Property Organization (WIPO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WIPO Patent Report: Statistics on Worldwide Patent Activity, World Intellectual Property Organization (WIPO), note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. Indicator IP.PAT.RESD (https://data.worldbank.org/indicator/IP.PAT.RESD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IP.PAT.RESD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ip_tmk_nrct": { "title": "Trademark applications, nonresident, by count", "description_from_producer": "A trademark is a sign capable of distinguishing the goods or services of one enterprise from those of other enterprises. Trademarks are protected by intellectual property rights. Non-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled. Class count is used to render application data for trademark applications across offices comparable, as some offices follow a single-class/single-design filing system while other have a multiple class/design filing system.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\nStatistical concept(s): Trademark: A sign used to distinguish the goods or services of one undertaking from those of another. A trademark may consist of words and combinations of words (for instance, names or slogans), logos, figures and images, letters, numbers, sounds, or, in rare instances, smells or moving images, or a combination thereof. The procedures for registering trademarks are governed by the legislation and procedures of national and regional IP offices and WIPO. Trademark rights are limited to the jurisdiction of the IP office that registers the trademark. Trademarks can be registered by filing an application at the relevant national or regional office(s), or by filing an international application through the Madrid System.\n\nNon-resident application refers to an application filed with the IP office of or acting on behalf of a state or jurisdiction in which the first-named applicant in the application is not domiciled.\n\nClass count: The number of classes specified in a trademark application or registration. In the international trademark system, and at certain national and regional offices, an applicant can file a trademark application specifying one or more of the 45 goods and services classes of the Nice Classification. Offices use either a multi-class or a single filing system. For example, the offices of Japan, the Republic of Korea and the United States of America (US), as well as many European IP offices, have multi-class filing systems. On the other hand, the offices of Brazil, Mexico, and South Africa follow a single-class filing system, requiring a separate application for each class in which an applicant seeks trademark protection. To capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration class counts.\n\n### Development relevance:\nThe progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself.\n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used.\n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone \u2013 including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries \u2013 which bring pleasure to millions of people worldwide \u2013 would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)\n\n### Limitations and exceptions:\nData are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods.", "origins": [ { "producer": "World Intellectual Property Organization (WIPO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. Indicator IP.TMK.NRCT (https://data.worldbank.org/indicator/IP.TMK.NRCT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IP.TMK.NRCT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ip_tmk_rsct": { "title": "Trademark applications, resident, by count", "description_from_producer": "A trademark is a sign capable of distinguishing the goods or services of one enterprise from those of other enterprises. Trademarks are protected by intellectual property rights. A resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. Class count is used to render application data for trademark applications across offices comparable, as some offices follow a single-class/single-design filing system while other have a multiple class/design filing system.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Information about the World Intellectual Property Organization (WIPO) data collection can be accessed on the WIPO website: https://www.wipo.int/en/web/ip-statistics/about\nStatistical concept(s): Trademark: A sign used to distinguish the goods or services of one undertaking from those of another. A trademark may consist of words and combinations of words (for instance, names or slogans), logos, figures and images, letters, numbers, sounds, or, in rare instances, smells or moving images, or a combination thereof. The procedures for registering trademarks are governed by the legislation and procedures of national and regional IP offices and WIPO. Trademark rights are limited to the jurisdiction of the IP office that registers the trademark. Trademarks can be registered by filing an application at the relevant national or regional office(s), or by filing an international application through the Madrid System.\n\nFor statistical purposes, a resident application refers to an application filed with the IP office of, or acting for, the state or jurisdiction in which the first named applicant in the application is resident. For example, an application filed with the Japan Patent Office (JPO) by a resident of Japan is considered a resident application from the perspective of the JPO. Resident applications are sometimes referred to as \u201cdomestic applications.\u201d A resident grant/registration is an IP right issued on the basis of a resident application.\n\nClass count: The number of classes specified in a trademark application or registration. In the international trademark system, and at certain national and regional offices, an applicant can file a trademark application specifying one or more of the 45 goods and services classes of the Nice Classification. Offices use either a multi-class or a single filing system. For example, the offices of Japan, the Republic of Korea and the United States of America (US), as well as many European IP offices, have multi-class filing systems. On the other hand, the offices of Brazil, Mexico, and South Africa follow a single-class filing system, requiring a separate application for each class in which an applicant seeks trademark protection. To capture the differences in application and registration numbers across offices, it is useful to compare their respective application and registration class counts.\n\n### Development relevance:\nThe progress and well-being of humanity depend on our capacity to come up with new ideas and creations. Technological progress requires the development and application of new inventions, while a vibrant culture will constantly seek new ways to express itself.\n\nIntellectual property rights are also vital. Inventors, artists, scientists and businesses put a lot of time, money, energy and thought into developing their innovations and creations. To encourage them to do that, they need the chance to make a fair return on their investment. That means giving them rights to protect their intellectual property. Essentially, intellectual property rights such as copyright, patents and trademarks can be viewed like any other property right. They allow the creators or owners of IP to benefit from their work or from their investment in a creation by giving them control over how their property is used.\n\nIP rights have long been recognized within various legal systems. For example, patents to protect inventions were granted in Venice as far back as the fifteenth century. Modern initiatives to protect IP through international law started with the Paris Convention for the Protection of Industrial Property (1883) and the Berne Convention for the Protection of Literary and Artistic Works (1886). These days, there are more than 25 international treaties on IP administered by WIPO. IP rights are also safeguarded by Article 27 of the Universal Declaration of Human Rights.\n\nCreativity and inventiveness are vital. They spur economic growth, create new jobs and industries, and enhance the quality and enjoyment of life. The intellectual property system needs to balance the rights and interests of different groups: of creators and consumers; of businesses and their competitors; of high- and low-income countries. An efficient and fair IP system benefits everyone \u2013 including ordinary users and consumers.\n\nSome examples: (a) The multibillion-dollar film, recording, publishing and software industries \u2013 which bring pleasure to millions of people worldwide \u2013 would not thrive without copyright protection.\n(b) The patent system rewards researchers and inventors while also ensuring that they share their knowledge by making patent applications publicly available, which helps stimulate more innovation. (c) Trademark protection discourages counterfeiting, so businesses can compete on a level playing field and users can be confident they are buying the genuine article.\n\n(source: https://doi.org/10.34667/tind.42176)\n\n### Limitations and exceptions:\nData are based on information supplied to World Intellectual Property Organization (WIPO) by IP offices in annual surveys, supplemented by data in national IP office reports. Data may be missing for some offices or periods.", "origins": [ { "producer": "World Intellectual Property Organization (WIPO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Statistics Database, World Intellectual Property Organization (WIPO), uri: www.wipo.int/ipstats/, note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. Indicator IP.TMK.RSCT (https://data.worldbank.org/indicator/IP.TMK.RSCT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IP.TMK.RSCT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "iq_cpa_breg_xq": { "title": "CPIA business regulatory environment rating (1=low to 6=high)", "description_from_producer": "The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis Business Regulatory Environment criterion assesses the extent to which the legal, regulatory, and policy environment helps or hinders private business in investing, creating jobs, and becoming more productive. The\nemphasis is on direct regulations of business activity and regulation of goods and factor markets. Three sub-components are measured: (a) regulations affecting entry, exit, and competition; (b) regulations of ongoing business operations; and (c) regulations of factor markets (labor and land).\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.BREG.XQ (https://data.worldbank.org/indicator/IQ.CPA.BREG.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.BREG.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_debt_xq": { "title": "CPIA debt policy rating (1=low to 6=high)", "description_from_producer": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Debt Policy and Management criterion assesses whether the country\u2019s debt management strategy is conducive to ensure medium-term debt sustainability and minimize budgetary risks. The criterion covers: (a) the extent to which external and domestic debt is contracted with a view to achieving/maintaining debt sustainability; and (b) the effectiveness of debt management functions (including the degree of coordination between debt management and other macroeconomic policies, the effectiveness of the debt management unit, and the existence of a debt management strategy and of a legal framework for borrowing).\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nEach of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\nRefer to Other notes for the Statistical Concept(s).\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Other notes:\nStatistical concept(s): The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.DEBT.XQ (https://data.worldbank.org/indicator/IQ.CPA.DEBT.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.DEBT.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_econ_xq": { "title": "CPIA economic management cluster average (1=low to 6=high)", "description_from_producer": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Economic Management cluster includes monetary and exchange rate policies, fiscal policy, and debt policy.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.ECON.XQ (https://data.worldbank.org/indicator/IQ.CPA.ECON.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.ECON.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_envr_xq": { "title": "CPIA policy and institutions for environmental sustainability rating (1=low to 6=high)", "description_from_producer": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis criterion assesses the extent to which environmental policies and institutions foster the protection and sustainable use of natural resources and the management of pollution.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.ENVR.XQ (https://data.worldbank.org/indicator/IQ.CPA.ENVR.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.ENVR.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_finq_xq": { "title": "CPIA quality of budgetary and financial management rating (1=low to 6=high)", "description_from_producer": "The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe quality of budgetary and financial management criterion assesses the extent to which there is: (a) a comprehensive and credible budget, linked to policy priorities; (b) effective financial management systems to ensure that the budget is implemented as intended in a controlled and predictable way; and (c) timely and accurate accounting and fiscal reporting, including timely audit of public accounts and effective arrangements for follow up.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.FINQ.XQ (https://data.worldbank.org/indicator/IQ.CPA.FINQ.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.FINQ.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_fins_xq": { "title": "CPIA financial sector rating (1=low to 6=high)", "description_from_producer": "The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe financial sector criterion assesses the policies and regulations that affect financial sector development. Three dimensions are covered: (a) financial stability; (b) the sector\u2019s efficiency, depth, and resource mobilization strength; and (c) access to financial services.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: http://www.worldbank.org/ida. Indicator IQ.CPA.FINS.XQ (https://data.worldbank.org/indicator/IQ.CPA.FINS.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.FINS.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_fisp_xq": { "title": "CPIA fiscal policy rating (1=low to 6=high)", "description_from_producer": "The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis CPIA fiscal policy criterion assesses the quality of the fiscal policy in its stabilization and allocation functions. The stabilization function deals with achieving macroeconomic policy objectives in conjunction with coherent monetary and exchange rate policies\u2014smoothing business cycle fluctuations, accommodating shocks. The allocation function is concerned with the appropriate provision of public goods. The criterion pays attention to public expenditure composition, including, for example, the provision of public infrastructure and agriculture related public goods and services that support medium-term growth.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nEach of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\nRefer to Other notes for the Statistical Concept(s).\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.\n\n### Other notes:\nStatistical concept(s): The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.FISP.XQ (https://data.worldbank.org/indicator/IQ.CPA.FISP.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.FISP.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_gndr_xq": { "title": "CPIA gender equality rating (1=low to 6=high)", "description_from_producer": "The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA gender equality rating criterion assesses the extent to which the country has enacted and put in place institutions and programs to enforce laws and policies that: (a) promote equal access for men and women to human capital development; (b) promote equal access for men and women to productive and economic resources; and (c) give men and women equal status and protection under the law. For the human capital development dimension, the focus is on primary completion and access to secondary education, access to health care during delivery and to family planning, and adolescent fertility rate. For access to economic and productive resources, the focus is on labor force participation, land tenure and property and inheritance rights. For Agency for change and equalization of status and protection under the law the focus is on individual and family rights and personal security (violence against women, trafficking, or sexual harassment) and political participation.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.GNDR.XQ (https://data.worldbank.org/indicator/IQ.CPA.GNDR.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.GNDR.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_hres_xq": { "title": "CPIA building human resources rating (1=low to 6=high)", "description_from_producer": "The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA building human resources criterion assesses the national policies and public and private sector service delivery that affect access to and quality of health and education-related services. The criterion has two components: (a) health, including population and reproductive health, and nutrition as well as the prevention and treatment of communicable diseases such as HIV/AIDS, tuberculosis, and malaria; and (b) education, training and literacy programs, and early child development (ECD) programs, including both formal and non-formal programs (which may combine education, health, and nutrition interventions) aimed at children aged 0-6.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.HRES.XQ (https://data.worldbank.org/indicator/IQ.CPA.HRES.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.HRES.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_irai_xq": { "title": "IDA resource allocation index (1=low to 6=high)", "description_from_producer": "The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score (the IDA resource allocation index) and scores for sixteen criteria that compose the CPIA.\n\nThese criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country's performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country's actual performance on each of the criteria and assigns a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank's concessional lending (lending by the World Bank Group's International Development Association (IDA) on terms with significant grace periods, long repayment periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country's IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country's overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.IRAI.XQ (https://data.worldbank.org/indicator/IQ.CPA.IRAI.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.IRAI.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_macr_xq": { "title": "CPIA macroeconomic management rating (1=low to 6=high)", "description_from_producer": "The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA macroeconomic management cluster assesses the monetary, exchange rate, and fiscal policy, as well as debt policy and management.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nEach of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\nRefer to Other notes for the Statistical Concept(s).\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.\n\n### Other notes:\nStatistical concept(s): The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nFor each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.MACR.XQ (https://data.worldbank.org/indicator/IQ.CPA.MACR.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.MACR.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_padm_xq": { "title": "CPIA quality of public administration rating (1=low to 6=high)", "description_from_producer": "The CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe CPIA Quality of Public Administration criterion covers the core administration defined as the civilian central government (and subnational governments, to the extent that their size or policy responsibilities are significant) excluding health and education personnel, and police. The criterion assesses the functioning of the core administration in three areas: (a) managing its own operations; (b) ensuring quality in policy implementation and regulatory management; and (c) coordinating the larger public sector Human Resources Management regime outside the core administration (de-concentrated and arms-length bodies and subsidiary governments).\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.PADM.XQ (https://data.worldbank.org/indicator/IQ.CPA.PADM.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.PADM.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_pres_xq": { "title": "CPIA equity of public resource use rating (1=low to 6=high)", "description_from_producer": "Equity of public resource use assesses the extent to which the pattern of public expenditures and revenue collection affects the poor and is consistent with national poverty reduction priorities.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: All criteria within each cluster receive equal weight, and each cluster has a 25 percent weight in the overall score, which is obtained by averaging the average scores of the four clusters. For each of the 16 criteria countries are rated on a scale of 1 (low) to 6 (high). The scores depend on the level of performance in a given year assessed against the criteria, rather than on changes in performance compared with the previous year. All 16 CPIA criteria contain a detailed description of each rating level. In assessing country performance, World Bank staff evaluate the country's performance on each of the criteria and assign a rating. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge and on relevant publicly available indicators. In interpreting the assessment scores, it should be noted that the criteria are designed in a developmentally neutral manner. Accordingly, higher scores can be attained by a country that, given its stage of development, has a policy and institutional framework that more strongly fosters growth and poverty reduction.\n\nThe country teams that prepare the ratings are very familiar with the country, and their assessments are based on country diagnostic studies prepared by the World Bank or other development organizations and on their own professional judgment. An early consultation is conducted with country authorities to make sure that the assessments are informed by up-to-date information. To ensure that scores are consistent across countries, the process involves two key phases. In the benchmarking phase a small representative sample of countries drawn from all regions is rated. Country teams prepare proposals that are reviewed first at the regional level and then in a Bankwide review process. A similar process is followed to assess the performance of the remaining countries, using the benchmark countries' scores as guideposts. The final ratings are determined following a Bankwide review. The overall numerical IRAI score and the separate criteria scores were first publicly disclosed in June 2006.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.PRES.XQ (https://data.worldbank.org/indicator/IQ.CPA.PRES.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.PRES.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_prop_xq": { "title": "CPIA property rights and rule-based governance rating (1=low to 6=high)", "description_from_producer": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Property Rights and Rule-Based Governance criterion assesses the extent to which economic activity is facilitated by an effective legal system and rule-based governance structure in which property and contract rights are reliably respected and enforced. It encompasses three dimensions: (a) legal framework for secure property and contract rights, including predictability and impartiality of laws and regulations; (b) quality of the legal and judicial system, as measured by independence, accessibility, legitimacy, efficiency, transparency, and integrity of the courts and other relevant dispute resolution mechanisms; and (c) crime and violence as an impediment to economic\nactivity and citizen security.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.PROP.XQ (https://data.worldbank.org/indicator/IQ.CPA.PROP.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.PROP.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_prot_xq": { "title": "CPIA social protection rating (1=low to 6=high)", "description_from_producer": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Social Protection criterion assesses government policies in social protection and labor market regulations that reduce the risk of becoming poor, assist those who are poor to better manage further risks, and ensure a minimal level of welfare to all people. Specifically it evaluates social protection (SP) and labor policies, namely those engaged in risk prevention by supporting savings and risk pooling through social insurance, protection against destitution through redistributive safety net programs and promotion of human capital development and income generation, including labor market programs. It also assesses the functioning of an SP system, including its effectiveness in a crisis and in providing arrangements and incentives to help beneficiaries to move from protection to promotion and prevention, including through interactions with private, informal means of SP. The criterion covers: (a) the overall SP system; (b) social safety net programs; (c) labor markets programs and policies, namely those aiming to promote employment creation and productivity growth while protecting core labor standards and ensuring adequate working conditions; (d) local service delivery and civil society participation in community development programs; and (e) pension and old age savings programs.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country's performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country's actual performance on each of the criteria and assigns a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.PROT.XQ (https://data.worldbank.org/indicator/IQ.CPA.PROT.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.PROT.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_pubs_xq": { "title": "CPIA public sector management and institutions cluster average (1=low to 6=high)", "description_from_producer": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Public Sector Management and Institutions cluster includes property rights and rule-based governance, quality of budgetary and financial management, efficiency of revenue mobilization, quality of public administration, and transparency, accountability, and corruption in the public sector.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.PUBS.XQ (https://data.worldbank.org/indicator/IQ.CPA.PUBS.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.PUBS.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_revn_xq": { "title": "CPIA efficiency of revenue mobilization rating (1=low to 6=high)", "description_from_producer": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThis Efficiency of Revenue Mobilization criterion assesses the overall pattern of revenue mobilization, not only the tax structure as it exists on paper, but revenue from all sources as they are collected. Separate sub-ratings\nare provided for (a) tax policy and (b) tax administration.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.REVN.XQ (https://data.worldbank.org/indicator/IQ.CPA.REVN.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.REVN.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_soci_xq": { "title": "CPIA policies for social inclusion/equity cluster average (1=low to 6=high)", "description_from_producer": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Policies for Social Inclusion and Equity cluster includes gender equality, equity of public resource use, building human resources, social protection and labor, and policies and institutions for environmental sustainability.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.SOCI.XQ (https://data.worldbank.org/indicator/IQ.CPA.SOCI.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.SOCI.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_strc_xq": { "title": "CPIA structural policies cluster average (1=low to 6=high)", "description_from_producer": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Structural Policies cluster includes trade, financial sector, and business regulatory environment.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.STRC.XQ (https://data.worldbank.org/indicator/IQ.CPA.STRC.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.STRC.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_trad_xq": { "title": "CPIA trade rating (1=low to 6=high)", "description_from_producer": "Trade assesses how the policy framework fosters trade in goods.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: All criteria within each cluster receive equal weight, and each cluster has a 25 percent weight in the overall score, which is obtained by averaging the average scores of the four clusters. For each of the 16 criteria countries are rated on a scale of 1 (low) to 6 (high). The scores depend on the level of performance in a given year assessed against the criteria, rather than on changes in performance compared with the previous year. All 16 CPIA criteria contain a detailed description of each rating level. In assessing country performance, World Bank staff evaluate the country's performance on each of the criteria and assign a rating. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge and on relevant publicly available indicators. In interpreting the assessment scores, it should be noted that the criteria are designed in a developmentally neutral manner. Accordingly, higher scores can be attained by a country that, given its stage of development, has a policy and institutional framework that more strongly fosters growth and poverty reduction.\n\nThe country teams that prepare the ratings are very familiar with the country, and their assessments are based on country diagnostic studies prepared by the World Bank or other development organizations and on their own professional judgment. An early consultation is conducted with country authorities to make sure that the assessments are informed by up-to-date information. To ensure that scores are consistent across countries, the process involves two key phases. In the benchmarking phase a small representative sample of countries drawn from all regions is rated. Country teams prepare proposals that are reviewed first at the regional level and then in a Bankwide review process. A similar process is followed to assess the performance of the remaining countries, using the benchmark countries' scores as guideposts. The final ratings are determined following a Bankwide review. The overall numerical IRAI score and the separate criteria scores were first publicly disclosed in June 2006.\n\n### Development relevance:\nThe International Development Association (IDA) is the part of the World Bank Group that helps the poorest countries reduce poverty by providing concessional loans and grants for programs aimed at boosting economic growth and improving living conditions. IDA funding helps these countries deal with the complex challenges they face in meeting the Millennium Development Goals.\n\nThe World Bank's IDA Resource Allocation Index (IRAI) is based on the results of the annual Country Policy and Institutional Assessment (CPIA) exercise, which covers the IDA-eligible countries. Country assessments have been carried out annually since the mid-1970s by World Bank staff. Over time the criteria have been revised from a largely macroeconomic focus to include governance aspects and a broader coverage of social and structural dimensions. Country performance is assessed against a set of 16 criteria grouped into four clusters: economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions. IDA resources are allocated to a country on per capita terms based on its IDA country performance rating and, to a limited extent, based on its per capita gross national income. This ensures that good performers receive a higher IDA allocation in per capita terms. The IRAI is a key element in the country performance rating.\n\n### Limitations and exceptions:\nThe CPIA exercise is intended to capture the quality of a country's policies and institutional arrangements, focusing on key elements that are within the country's control, rather than on outcomes (such as economic growth rates) that are influenced by events beyond the country's control. More specifically, the CPIA measures the extent to which a country's policy and institutional framework supports sustainable growth and poverty reduction and, consequently, the effective use of development assistance.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: http://www.worldbank.org/ida. Indicator IQ.CPA.TRAD.XQ (https://data.worldbank.org/indicator/IQ.CPA.TRAD.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.TRAD.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_cpa_tran_xq": { "title": "CPIA transparency, accountability, and corruption in the public sector rating (1=low to 6=high)", "description_from_producer": "The Country Policy and Institutional Assessment (CPIA) measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for sixteen criteria that compose the CPIA. These criteria include: A. Economic Management (1. Monetary and Exchange Rate Policies; 2. Fiscal Policy; 3. Debt Policy and Management), B. Structural Policies (4. Trade; 5. Financial Sector; 6. Business Regulatory Environment), C. Policies for Social Inclusion/Equity (7. Gender equality; 8. Equity of public resource use; 9. Building human resources; 10. Social protection and labor; 11. Policies and institutions for environmental sustainability), D. Public Sector Management and Institutions (12. Property rights and rule-based governance; 13. Quality of budgetary and financial management; 14. Efficiency of revenue mobilization; 15. Quality of public administration; 16. Transparency, accountability, and corruption in the public sector).\n\nThe Transparency, Accountability, and Corruption in the Public Sector criterion assesses the extent to which the executive, legislators, and other high-level officials can be held accountable for their use of funds, administrative decisions, and results obtained. Accountability is generally enhanced by transparency in decision-making, access to relevant and timely information, public and media scrutiny, and by institutional checks (e.g., inspector general, ombudsman, or independent audit) on the authority of the chief executive. The criterion covers four dimensions: (a) the accountability of the executive and other top officials to effective oversight institutions; (b) access of civil society to timely and reliable information on public affairs and public policies, including fiscal information (on public expenditures, revenues, and large contract awards); (c) state capture by narrow vested interests; and (d) integrity in the management of public resources, including aid and natural resource revenues.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Each of the four clusters has a 25 percent weight in the overall rating. Within each cluster, all criteria receive equal weight, although components within a criterion may be weighted differently. The overall score is obtained by calculating the average score for each cluster, and then by averaging the scores of the four clusters. The CPIA can then be interpreted as representing an overall country score that considers each of the four clusters to be equally relevant even if some of the clusters contain more criteria than others.\n\nFor each of the criteria, the Bank has prepared guidance to help staff assess the country\u2019s performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assesses the country\u2019s actual performance on each of the criteria and assign a rating. These scores are averaged\u2014first to yield the cluster score, and then to determine a composite country rating as the average of the four clusters. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge originated in the Bank or elsewhere, and on relevant publicly available indicators.\nStatistical concept(s): The CPIA consists of 16 criteria grouped in four equally weighted clusters: Economic Management, Structural Policies, Policies for Social Inclusion and Equity, and Public Sector Management and Institutions. For each of the 16 criteria, countries are rated on a scale of 1 to 6. A rating of 1 corresponds to a very weak performance, and a rating of 6 to a very strong performance. Intermediate scores of 1.5, 2.5, 3.5, 4.5, and 5.5 may also be given.\n\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.\n\n### Development relevance:\nThe CPIA measures the extent to which a country\u2019s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. The outcome of the exercise yields both an overall score and scores for all of the sixteen criteria that compose the CPIA. Experience has taught the development community that good policies and institutions lead, over time, to favorable growth and poverty reduction outcomes, notwithstanding possible yearly fluctuations arising from internal and external factors. The CPIA ratings help determine the relative sizes of the Bank\u2019s concessional lending (lending by the World Bank Group\u2019s International Development Association (IDA) on terms with significant grace periods, long repayments periods, and very low-interest rates) and grants to low-income countries. IDA resources are allocated in per capita terms based on a country\u2019s IDA country performance rating (CPR) and, to a limited extent, per capita gross national income (GNI). Use of the CPR ensures that good performers receive, in per capita terms, a higher IDA allocation \u2014 allocations are performance based. A country\u2019s overall score is the main element of the CPR. To fully underscore this role, the overall CPIA country score is referred to as the IDA Resource Allocation Index (IRAI).\n\n### Limitations and exceptions:\nThe scores depend on the level of performance each year assessed against the criteria, rather than on changes in performance compared to the previous year. The ratings depend on actual policies and performance, rather than on promises or intentions. In some cases, measures such as the passage of specific legislation can represent an important action that deserves consideration. However, the way such actions should be factored into the ratings is carefully assessed, because in the end, it is the implementation of legislation that determines the extent of its impact.", "origins": [ { "producer": "CPIA database - World Bank Group", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "CPIA database, World Bank Group (WBG), uri: https://datacatalog.worldbank.org/int/search/dataset/0038988. Indicator IQ.CPA.TRAN.XQ (https://data.worldbank.org/indicator/IQ.CPA.TRAN.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.CPA.TRAN.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 6=high", "display": { "unit": "1=low to 6=high" } }, "iq_spi_ovrl": { "title": "Statistical performance indicators (SPI): Overall score (scale 0-100)", "description_from_producer": "The SPI overall score is a composite score measuring country performance across five pillars: data use, data services, data products, data sources, and data infrastructure. The new Statistical Performance Indicators (SPI) will replace the Statistical Capacity Index (SCI), which the World Bank has regularly published since 2004. Although the goals are the same, to offer a better tool to measure the statistical systems of countries, the new SPI framework has expanded into new areas including in the areas of data use, administrative data, geospatial data, data services, and data infrastructure. The SPI provides a framework that can help countries measure where they stand in several dimensions and offers an ambitious measurement agenda for the international community.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Weighted average of all statistical performance indicators. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index\n\n### Development relevance:\nThe new Statistical Performance Indicators (SPI) will replace the Statistical Capacity Index (SCI), which the World Bank has regularly published since 2004. Although the goals are the same, to offer a better tool to measure the statistical systems of countries, the new SPI framework has expanded into new areas including in the areas of data use, administrative data, geospatial data, data services, and data infrastructure. The SPI provides a framework that can help countries measure where they stand in several dimensions and offers an ambitious measurement agenda for the international community.\n\n### Limitations and exceptions:\nLike all cross-country benchmarking exercises the HCI has limitations.\n\n\n\nComponents of the HCI such as stunting and test scores are measured only infrequently in some countries, and not at all in others. Other components, like child and adult survival rates, are imprecisely estimated in countries where vital registries are incomplete or non-existent. Data on enrollment rates needed to estimate expected years of school often have many gaps and are reported with significant lags. As a result, the HCI for a country may rely on measures that are somewhat dated that do not reflect the most up-to-date state of human capital in a country.\n\n\n\nThe test score harmonization exercise draws on test scores that come from different international testing programs and converts these into common units. However, the age of test takers and the subjects covered vary across testing programs. As a result, harmonized scores may reflect differences in sampling and cohorts participating in tests (Liu and Steiner-Khamsi 2020). Moreover, test scores may not accurately reflect the quality of the whole education system in a country to the extent that tests-takers are not representative of the population of all students. Reliable measures of the quality of tertiary education do not yet exist, despite the importance of higher education for human capital in a rapidly changing world. The index also does not explicitly capture other important aspect of human capital, such as noncognitive skills, although they may contribute directly and indirectly to human capital formation (see, for example, Lundberg 2018).\n\n\n\nOne objective of the HCI is to call attention to these data shortcomings and to galvanize action to remedy them. Improving data will take time. In the interim, and recognizing these limitations, the HCI should be interpreted with caution. The HCI provides rough estimates of how current education and health will shape the productivity of future workers and not a finely graduated measure of small differences between countries.", "origins": [ { "producer": "Statistical Performance Indicators, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators. Indicator IQ.SPI.OVRL (https://data.worldbank.org/indicator/IQ.SPI.OVRL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.SPI.OVRL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "", "display": { "numDecimalPlaces": 1 } }, "iq_spi_pil1": { "title": "Statistical performance indicators (SPI): Pillar 1 data use score (scale 0-100)", "description_from_producer": "The data use overall score is a composite score measuring the demand side of the statistical system. The data use pillar is segmented by five types of users: (i) the legislature, (ii) the executive branch, (iii) civil society (including sub-national actors), (iv) academia and (v) international bodies. Each dimension would have associated indicators to measure performance. A mature system would score well across all dimensions whereas a less mature one would have weaker scores along certain dimensions. The gaps would give insights into prioritization among user groups and help answer questions as to why the existing services are not resulting in higher use of national statistics in a particular segment. Currently, the SPI only features indicators for one of the five dimensions of data use, which is data use by international organizations. Indicators on whether statistical systems are providing useful data to their national governments (legislature and executive branches), to civil society, and to academia are absent. Thus the dashboard does not yet assess if national statistical systems are meeting the data needs of a large swathe of users.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Weighted average of statistical performance indicators related to data use. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index\n\n### Development relevance:\nThe data use (outcome) pillar is segmented by five types of users: (i) the legislature, (ii) the executive branch, (iii) civil society (including sub-national actors), (iv) academia and (v) international bodies. Each dimension would have associated indicators to measure performance. A mature system would score well across all dimensions whereas a less mature one would have weaker scores along certain dimensions. The gaps would give insights into prioritization among user groups and help answer questions as to why the existing services are not resulting in higher use of national statistics in a particular segment.\n\n### Limitations and exceptions:\nCurrently, the dashboard only features indicators for one of the five dimensions of data use, which is data use by international organizations. Indicators on whether statistical systems are providing useful data to their national governments (legislature and executive branches), to civil society, and to academia are absent. Thus the dashboard does not yet assess if national statistical systems are meeting the data needs of a large swathe of users.", "origins": [ { "producer": "Statistical Performance Indicators, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators. Indicator IQ.SPI.PIL1 (https://data.worldbank.org/indicator/IQ.SPI.PIL1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.SPI.PIL1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "iq_spi_pil2": { "title": "Statistical performance indicators (SPI): Pillar 2 data services score (scale 0-100)", "description_from_producer": "The data services pillar overall score is a composite indicator based on four dimensions of data services: (i) the quality of data releases, (ii) the richness and openness of online access, (iii) the effectiveness of advisory and analytical services related to statistics, and (iv) the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including input to national data strategies, advice on data ethics and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Weighted average of statistical performance indicators related to data services. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index\n\n### Development relevance:\nThe data services (output) pillar is segmented by four service types: (i) the quality of data releases, (ii) the richness and openness of online access, (iii) the effectiveness of advisory and analytical services related to statistics, and (iv) the availability and use of data access services such as secure microdata access. Advisory and analytical services might incorporate elements related to data stewardship services including input to national data strategies, advice on data ethics and calling out misuse of data in accordance with the Fundamental Principles of Official Statistics.\n\n### Limitations and exceptions:\nUnder the pillar of data services an area that needs improvement is the measurement of advisory and analytical services provided by NSOs, such as data stewardship services. By measuring this type of work done by NSOs that goes beyond producing data, the international community and the NSOs themselves can better assess whether this type of support is in place.", "origins": [ { "producer": "Statistical Performance Indicators, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators. Indicator IQ.SPI.PIL2 (https://data.worldbank.org/indicator/IQ.SPI.PIL2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.SPI.PIL2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "iq_spi_pil3": { "title": "Statistical performance indicators (SPI): Pillar 3 data products score (scale 0-100)", "description_from_producer": "The data products overall score is a composite score measuring whether the country is able to produce relevant indicators, primarily related to SDGs. The data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system's performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Weighted average of statistical performance indicators related to data products. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index\n\n### Development relevance:\nThe data products (internal process) pillar is segmented by four topics and organized into (i) social, (ii) economic, (iii) environmental, and (iv) institutional dimensions using the typology of the Sustainable Development Goals (SDGs). This approach anchors the national statistical system\u2019s performance around the essential data required to support the achievement of the 2030 global goals, and enables comparisons across countries so that a global view can be generated while enabling country specific emphasis to reflect the user needs of that country.", "origins": [ { "producer": "Statistical Performance Indicators, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators. Indicator IQ.SPI.PIL3 (https://data.worldbank.org/indicator/IQ.SPI.PIL3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.SPI.PIL3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "iq_spi_pil4": { "title": "Statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100)", "description_from_producer": "The data sources overall score is a composite measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country's institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Weighted average of statistical performance indicators related to data sources. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index\n\n### Development relevance:\nThe data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country\u2019s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.\n\n### Limitations and exceptions:\nIn the data sources pillar, more information is needed in the areas of administrative data, geospatial data, and private and citizen generated data. On administrative data, the picture is incomplete with no measures of whether countries have administrative data systems in place to measure health, education, labor, and social protection program statistics. For the geospatial indicator, there is a proxy measure of whether the country is able to produce indicators at the sub-national level, but as yet, no understanding of how countries are using geospatial information in other ways, for instance using satellite data. And while the world is increasingly awash with private and citizen generated data (e.g., on mobility, job search, or social networking), on a global scale there is no reliable source to measure how national statistical systems are incorporating this information.", "origins": [ { "producer": "Statistical Performance Indicators, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators. Indicator IQ.SPI.PIL4 (https://data.worldbank.org/indicator/IQ.SPI.PIL4). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.SPI.PIL4", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "iq_spi_pil5": { "title": "Statistical performance indicators (SPI): Pillar 5 data infrastructure score (scale 0-100)", "description_from_producer": "The data infrastructure pillar overall score measures the hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Weighted average of statistical performance indicators related to data infrastructure. Scores range from 0-100 with 100 representing the best score.\nStatistical concept(s): Composite Multi-dimensional Index\n\n### Development relevance:\nThe data infrastructure (capability) pillar includes hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors.\n\n### Limitations and exceptions:\nSeveral of the \u2018soft\u2019 components of the data infrastructure pillar lack adequate data. This includes the areas of skills and of partnerships between entities in the national statistical system. The dashboard makes use of the PARIS21 led SDG indicator on whether the statistical legislations in countries met the standards of the UN Fundamental Principles of Statistics, but this was not incorporated into the overall SPI score, because of inadequate country coverage. This is also true of the PARIS21 led SDG indicator on whether the national statistical system is fully funded. Countries would need to be encouraged to report on this information.", "origins": [ { "producer": "Statistical Performance Indicators, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Statistical Performance Indicators, World Bank (WB), uri: https://datacatalog.worldbank.org/dataset/statistical-performance-indicators. Indicator IQ.SPI.PIL5 (https://data.worldbank.org/indicator/IQ.SPI.PIL5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IQ.SPI.PIL5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "is_air_dprt": { "title": "Air transport, registered carrier departures worldwide", "description_from_producer": "Registered carrier departures worldwide are domestic takeoffs and takeoffs abroad of air carriers registered in the country.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The data is obtained from airports, airport operators, airport websites and/or civil aviation authorities. Data on passengers are reported annually by each State (for all its commercial air carriers, including scheduled and non-scheduled flights) via standardized forms sent to ICAO.\n\nEach passenger is counted once per flight number and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger.\n\nWhere some carriers do not report, ICAO may use historical reports or published flight-schedules to estimate their traffic.\nStatistical concept(s): Aircraft departures represent the number of take-offs of aircraft. For statistical purposes, departures are equal to the number of landings made or flight stages flown.\n\nA flight stage is the operation of an aircraft from take-off to its next landing. A flight stage is classified as either international or domestic. International flight stage is one or both terminals in the territory of a State, other than the State in which the air carrier has its principal place of business.\n\n### Development relevance:\nAviation traffic data are essential for understanding the role of air transport in economic development, global connectivity, and social progress. These statistics, covering passenger volumes, freight volumes, and aircraft departures, provide a standardized measure of air transport activity across countries and regions and are closely linked to economic growth, trade integration, tourism development, and labor mobility.\n\nAviation traffic indicators also help assess the resilience and efficiency of transport systems, track the recovery from economic shocks or crises, and identify capacity constraints or infrastructure investment needs.\n\nFrom a development perspective, aviation statistics support evidence-based policy making in areas such as transport planning, regional integration, climate and emissions management, and inclusive access to services, making them a key input for monitoring progress toward sustainable and connected economies.\n\n### Limitations and exceptions:\nWhile ICAO aviation data provide a globally standardized and comprehensive view of air transport activity, several limitations affect their accuracy and comparability. Data quality depends on the completeness and consistency of country reporting, which can vary due to differences in national statistical capacity, regulatory environments, and adherence to ICAO definitions. Some countries may not report all relevant data, leading ICAO to estimate missing values using historical submissions and published schedules, which can introduce uncertainty. Changes in airline registration, mergers, or operational practices may also affect the attribution of traffic statistics. Additionally, the aggregation of scheduled and non-scheduled services, as well as the treatment of transit and transfer passengers, may differ across countries, impacting cross-country comparability.", "origins": [ { "producer": "International Civil Aviation Organization (ICAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Civil Aviation Statistics of the World, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers;\nICAO Staff estimates, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers. Indicator IS.AIR.DPRT (https://data.worldbank.org/indicator/IS.AIR.DPRT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IS.AIR.DPRT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "is_air_good_mt_k1": { "title": "Air transport, freight (million ton-km)", "description_from_producer": "Air freight is the volume of freight, express, and diplomatic bags carried on each flight stage (operation of an aircraft from takeoff to its next landing), measured in metric tons times kilometers traveled.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The data is obtained from airports, airport operators, airport websites and/or civil aviation authorities. Data on passengers are reported annually by each State (for all its commercial air carriers, including scheduled and non-scheduled flights) via standardized forms sent to ICAO.\n\nEach passenger is counted once per flight number and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger.\n\nWhere some carriers do not report, ICAO may use historical reports or published flight-schedules to estimate their traffic.\nStatistical concept(s): A metric tonne of freight or mail carried one kilometre. Freight tonne-kilometres equal the sum of the products obtained by multiplying the number of tonnes of freight, express, diplomatic bags carried on each flight stage by the stage distance. For ICAO statistical purposes freight includes express and diplomatic bags but not passenger baggage. Mail tonne-kilometres are computed in the same way as freight tonne-kilometres.\n\n### Development relevance:\nAviation traffic data are essential for understanding the role of air transport in economic development, global connectivity, and social progress. These statistics, covering passenger volumes, freight volumes, and aircraft departures, provide a standardized measure of air transport activity across countries and regions and are closely linked to economic growth, trade integration, tourism development, and labor mobility.\n\nAviation traffic indicators also help assess the resilience and efficiency of transport systems, track the recovery from economic shocks or crises, and identify capacity constraints or infrastructure investment needs.\n\nFrom a development perspective, aviation statistics support evidence-based policy making in areas such as transport planning, regional integration, climate and emissions management, and inclusive access to services, making them a key input for monitoring progress toward sustainable and connected economies.\n\n### Limitations and exceptions:\nWhile ICAO aviation data provide a globally standardized and comprehensive view of air transport activity, several limitations affect their accuracy and comparability. Data quality depends on the completeness and consistency of country reporting, which can vary due to differences in national statistical capacity, regulatory environments, and adherence to ICAO definitions. Some countries may not report all relevant data, leading ICAO to estimate missing values using historical submissions and published schedules, which can introduce uncertainty. Changes in airline registration, mergers, or operational practices may also affect the attribution of traffic statistics. Additionally, the aggregation of scheduled and non-scheduled services, as well as the treatment of transit and transfer passengers, may differ across countries, impacting cross-country comparability.", "origins": [ { "producer": "International Civil Aviation Organization (ICAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Civil Aviation Statistics of the World, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers;\nICAO Staff estimates, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers. Indicator IS.AIR.GOOD.MT.K1 (https://data.worldbank.org/indicator/IS.AIR.GOOD.MT.K1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IS.AIR.GOOD.MT.K1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "million ton-km", "display": { "unit": "million ton-km" } }, "is_air_psgr": { "title": "Air transport, passengers carried", "description_from_producer": "Air carrier data per country refers to passengers carried by airlines registered in that country regardless of the origin or destination of the passengers.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The data is obtained from airports, airport operators, airport websites and/or civil aviation authorities. Data on passengers are reported annually by each State (for all its commercial air carriers, including scheduled and non-scheduled flights) via standardized forms sent to ICAO.\n\nEach passenger is counted once per flight number and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger.\n\nWhere some carriers do not report, ICAO may use historical reports or published flight-schedules to estimate their traffic.\nStatistical concept(s): The number of passengers carried is obtained by counting each passenger on a particular flight (with one flight number) once only and not repeatedly on each individual stage of that flight, with a single exception that a passenger flying on both the international and domestic stages of the same flight should be counted as both a domestic and an international passenger.\n\n### Development relevance:\nAviation traffic data are essential for understanding the role of air transport in economic development, global connectivity, and social progress. These statistics, covering passenger volumes, freight volumes, and aircraft departures, provide a standardized measure of air transport activity across countries and regions and are closely linked to economic growth, trade integration, tourism development, and labor mobility.\n\nAviation traffic indicators also help assess the resilience and efficiency of transport systems, track the recovery from economic shocks or crises, and identify capacity constraints or infrastructure investment needs.\n\nFrom a development perspective, aviation statistics support evidence-based policy making in areas such as transport planning, regional integration, climate and emissions management, and inclusive access to services, making them a key input for monitoring progress toward sustainable and connected economies.\n\n### Limitations and exceptions:\nWhile ICAO aviation data provide a globally standardized and comprehensive view of air transport activity, several limitations affect their accuracy and comparability. Data quality depends on the completeness and consistency of country reporting, which can vary due to differences in national statistical capacity, regulatory environments, and adherence to ICAO definitions. Some countries may not report all relevant data, leading ICAO to estimate missing values using historical submissions and published schedules, which can introduce uncertainty. Changes in airline registration, mergers, or operational practices may also affect the attribution of traffic statistics. Additionally, the aggregation of scheduled and non-scheduled services, as well as the treatment of transit and transfer passengers, may differ across countries, impacting cross-country comparability.", "origins": [ { "producer": "International Civil Aviation Organization (ICAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Civil Aviation Statistics of the World, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers;\nICAO Staff estimates, International Civil Aviation Organization (ICAO), uri: https://data.icao.int/newdataplus/#:~:text=ICAO%20data%20is%20comprised%20of,information%20about%20commercial%20air%20carriers. Indicator IS.AIR.PSGR (https://data.worldbank.org/indicator/IS.AIR.PSGR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IS.AIR.PSGR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "is_rrs_good_mt_k6": { "title": "Railways, goods transported (million ton-km)", "description_from_producer": "Goods transported by railway are the volume of goods transported by railway, measured in metric tons times kilometers traveled.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Freight traffic on any mode is typically measured in tons and ton-kilometers. A ton-kilometer equals cargo weight transported times distance transported. For railways, an important measure of work performed is gross ton-kilometers, this measure includes rail wagons' empty weight for both empty and loaded movements. This measure of gross ton-kilometers is also called \u2018trailing tons' or the total tons being hauled. Sometimes gross ton-kilometer measures include the weight of locomotives used to haul freight trains.\n\nThe indicator measures the tonne.kilometers of freight on the national territory of the railway.\n\nThe weight taken into account is the actual weight or chargeable weight of the goods carried. Weight means the quantity of goods in thousands of tonnes. The weight to be taken into consideration includes, in addition to the weight of the goods transported, the weight of packaging and the tare weight of containers, swap bodies, pallets as well as road vehicles transported by rail in the course of combined transport operations. If the goods are transported using the services of more than one railway undertaking (e.g. within the group etc.), when possible the weight of goods should not be counted more than once.\n\nThe number of tonne-kilometres in millions represents the weight of freight traffic (in millions of tonnes) over the charging distance (in kilometres).\n\nStatistical concept(s): Tonne-kilometre (tkm) is a unit of measurement of goods transport which represents the transport of one tonne of goods over a distance of one kilometre.\n\nThe distance to be covered is the distance actually travelled on the considered network. To avoid double counting each country should count only the tkm performed on its territory. If it is not available, then the distance charged or estimated should be taken into account.\n\n### Development relevance:\nTransport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe railway transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, and market liberalization affect road transport throughout the world.\n\nRailways have helped in the industrialization process of a country by easy transportation of coal and raw-materials at a cheaper rate. As railways require huge capital outlay, they may give rise to monopolies and work against public interest at large. Even if controlled and managed by the government, lack of competition sometimes results in inefficiency and high costs. Also, many times it is not economical to operate railways in sparsely settled rural areas. Thus, in many developing countries large rural areas have no railway even today.\n\nRail transport is a major form of passenger and freight transport in many countries. It is ubiquitous in Europe, with an integrated network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In the North America, freight rail transport is widespread and heavily used in for transporting goods. The western Europe region has the highest railway density in the world and has many individual trains which operate through several countries despite technical and organizational differences in each national network. Australia has a generally sparse network, mostly along its densely populated urban centers.\n\nBulk freight handling is a key advantage for rail transport. Low or even zero transshipment costs combined with energy efficiency and low inventory costs allow trains to handle bulk much cheaper than by road. Typical bulk cargo includes coal, ore, grains and liquids. Bulk goods can be transported in open-topped cars, hopper cars and tank cars. Container trains have become the dominant type in the US for non-bulk haulage.\n\n### Limitations and exceptions:\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\" The data from UIC is based on voluntary reporting by railway companies, and can show drastic increases or decreases for some of the years due to lack of reporting by some of the companies in that country.", "origins": [ { "producer": "International Union of Railways (UIC) and OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Railisa Database (UIC), International Union of Railways (UIC), uri: https://uic-stats.uic.org/select/;\nOECD Statistics, Organisation for Economic Co-operation and Development (OECD). Indicator IS.RRS.GOOD.MT.K6 (https://data.worldbank.org/indicator/IS.RRS.GOOD.MT.K6). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IS.RRS.GOOD.MT.K6", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "million ton-km", "display": { "unit": "million ton-km" } }, "is_rrs_pasg_km": { "title": "Railways, passengers carried (million passenger-km)", "description_from_producer": "Passengers carried by railway are the number of passengers transported by rail multiplied by kilometers traveled.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Passenger-kilometers are usually measured on the basis of the rail travel distance between origin and destination multiplied by the number of passengers traveling between each origin and destination. This variable relates to passengers, irrespective of the fare paid and also including free travelling passengers, but excluding members of the train crew.\n\nThe number of passengers should be calculated as number of passenger journeys. A journey is the act of moving from one place (origin) to another (destination) using a given transport mode (e.g. railway). A journey may consist of one or several stages depending on whether one has to change transport means (e.g. using more than one train) in order to get from the origin to the destination. In other words, a journey consists either of a single stage or a sequence of stages using the same transport mode (e.g. railway), following each other in such a way that the destination of one stage coincides with the origin of the next.\n\nJourneys should be considered finished when an overnight stay occurs. For practical purposes, journeys can be considered finished when a change in transport mode or transport company occurs.\n\nPassenger-kilometers is the total distance travelled by all the passengers. For instance, one person travelling for 20km contributes for 20 passenger-kilometres; four people, travelling for 20km each, contribute for 80 passenger- kilometers\n\nStatistical concept(s): A passenger kilometer is performed when a passenger is carried for one kilometer. Rail passenger-kilometer (pkm) is a unit of measurement representing the transport of one rail passenger by rail over a distance of one kilometer.\n\nThe distance to be taken into consideration should be the distance actually travelled by the passenger on the network. To avoid double counting each country should count only the pkm performed on its territory. If this is not available, then the distance charged or estimated should be used.\n\n### Development relevance:\nTransport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe railway transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses. Economic growth, technological change, and market liberalization affect road transport throughout the world.\n\nRailways have helped in the industrialization process of a country by easy transportation of coal and raw-materials at a cheaper rate. As railways require huge capital outlay, they may give rise to monopolies and work against public interest at large. Even if controlled and managed by the government, lack of competition sometimes results in inefficiency and high costs. Also, many times it is not economical to operate railways in sparsely settled rural areas. Thus, in many developing countries large rural areas have no railway even today.\n\nRail transport is a major form of passenger and freight transport in many countries. Passenger trains can involve a variety of functions including long distance travel, daily commuter trips, or local urban transit services. Railways are very popular mode of transportation in Europe, with an integrated network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In North America, freight rail transport is widespread and heavily used in for transporting goods. The western Europe region has the highest railway density in the world and has many individual trains which operate through several countries despite technical and organizational differences in each national network. Australia has a generally sparse network, mostly along its densely populated urban centers.\n\n### Limitations and exceptions:\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\" The data from UIC is based on voluntary reporting by railway companies, and can show drastic increases or decreases for some of the years due to lack of reporting by some of the companies in that country.", "origins": [ { "producer": "International Union of Railways (UIC) and OECD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Railisa Database (UIC), International Union of Railways (UIC), uri: https://uic-stats.uic.org/select/;\nOECD Statistics, Organisation for Economic Co-operation and Development (OECD). Indicator IS.RRS.PASG.KM (https://data.worldbank.org/indicator/IS.RRS.PASG.KM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IS.RRS.PASG.KM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "million passenger-km", "display": { "unit": "million passenger-km" } }, "is_rrs_totl_km": { "title": "Rail lines (total route-km)", "description_from_producer": "The total length of rail line in the country operated for passenger transport, goods transport, or both.\n\n### Statistical concept and methodology:\nMethodology: Rail lines are the length of railway route available for train service, irrespective of the number of parallel tracks. It includes railway routes that are open for public passenger and freight services and excludes dedicated private resource railways.\n\nGauge: N (standard gauge: 1,435 m); L (broad gauge: exact rail gauge inserted); E (narrow gauge: exact rail gauge inserted). The length of railway lines worked is obtained by taking these sections including main-line track listed in the Capital Expenditure Account.\n\nSections not worked are deducted only in cases where they are permanently out of use, that is, if they are no longer maintained in working order. Lines temporarily out of use continue to form part of the length of lines worked.\n\nThe length of a section is measured in the middle of the section, from center to the center of the passenger buildings, or of the corresponding service buildings, of stations which are shown as independent points of departure or arrival for the conveyance of passengers or freight. If the boundary of the rail network falls in open track, the length of the section is measured up to that point.\n\nThe section situated between a station approach and the join to the main line of two lines or more which is used by all trains in either direction over these lines, is only counted once. However, if for one or more of these lines, tracks are normally allocated, the length of these lines is counted separately.\n\nIf between two stations there are one or more parallel tracks (siding-lines) to the main line, only the length of the latter is counted.\n\nIn the case of regular lines worked exclusively during part of the year (seasonal lines), their length is included in the end-of-year statement (Var 1112: Total length of lines worked at the end of the year in the Railisa database).\nStatistical concept(s): Railway line: one or more adjacent running tracks forming a route between two points. Where a section of network comprises two or more lines running alongside one another, there are as many lines as routes to which tracks are allotted exclusively.\n\n### Development relevance:\nTransport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, governments, and the private sector. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nRailway transport is a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses.\n\nRailways have helped in the industrialization process by easy transportation of raw-materials at a cheaper rate. As railways require huge capital outlay, they may give rise to monopolies and work against public interest at large. Lack of competition sometimes results in inefficiency and high costs. Also, many times it is not economical to operate railways in sparsely settled rural areas. Thus, in many developing countries large rural areas have no railway even today.\n\nRail transport is a major form of passenger and freight transport in many countries. It is ubiquitous in Europe, with an integrated network covering virtually the whole continent. In India, China, South Korea and Japan, many millions use trains as regular transport. In the North America, freight rail transport is widespread and heavily used in for transporting goods. The western Europe region has the highest railway density in the world and has many individual trains which operate through several countries despite technical and organizational differences in each national network. Australia has a generally sparse network, mostly along its densely populated urban centers.\n\n### Limitations and exceptions:\nData for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized\". The data from UIC is based on voluntary reporting by railway companies, and can show drastic increases or decreases for some of the years due to lack of reporting by some of the companies in that country.", "origins": [ { "producer": "International Union of Railways (UIC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Railisa Database, International Union of Railways (UIC), uri: https://uic-stats.uic.org/. Indicator IS.RRS.TOTL.KM (https://data.worldbank.org/indicator/IS.RRS.TOTL.KM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IS.RRS.TOTL.KM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "total route-km", "display": { "unit": "total route-km" } }, "is_shp_gcnw_xq": { "title": "Liner shipping connectivity index (maximum value in 2004 = 100)", "description_from_producer": "The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on five components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country's ports. For each component a country's value is divided by the maximum value of each component in 2004, the five components are averaged for each country, and the average is divided by the maximum average for 2004 and multiplied by 100. The index generates a value of 100 for the country with the highest average index in 2004. . The underlying data come from Containerisation International Online.\n\n### Statistical concept and methodology:\nMethodology: The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. Starting in 2020 the index was improved and is published as a quarterly series with the index set at 100 for the country with the highest average in the first quarter of 2006. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on six components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, the number of country-pairs with a direct connection, and number of companies that deploy container ships in a country's ports. The data are derived from Containerisation International Online (www.ci-online.co.uk). For each of the six components, a country's value is divided by the maximum value of that component in Q1 2006, and for each country, the average of the six components is calculated. This average is then divided by the maximum average for Q1 2006 and multiplied by 100. In this way, the index generates the value 100 for the country with the highest average index of the six components in Q1 2006. Annual values of the index equal the values of the first quarter of the same corresponding year.\n\n### Development relevance:\nThe liner shipping connectivity index (LSCI) aims at capturing a country's integration level into global liner shipping networks. A country's access to world markets depends largely on their transport connectivity, especially in regard to regular shipping services for the import and export of manufactured goods.\n\nTrade facilitation encompasses customs efficiency and other physical and regulatory environments where trade takes place, harmonization of standards and conformance to international regulations, and the logistics of moving goods and associated documentation through countries and ports. Though collection of trade facilitation data has improved over the last decade, data that allow meaningful evaluation, especially for developing economies, are lacking. The quality and accessibility of ports and roads affect logistics performance.\n\nAccess to global shipping and air freight networks and the quality and accessibility of ports and roads affect logistics performance. Maritime transport is the backbone of international trade and a key engine driving globalization. Around 80 per cent of global trade by volume and over 70 per cent by value is carried by sea and is handled by ports worldwide; these shares are even higher in the case of most developing countries.\n\nA total of 60 per cent of world seaborne trade by volume is loaded, and 57 per cent unloaded, in developing-country ports. That is a remarkable shift away from previous patterns, in which developing economies served mainly as loading areas for raw materials and natural resources.\n\n### Limitations and exceptions:\nData on trade facilitation are drawn from research by private and international agencies. Most data are perception-based evaluations by business executives and professionals. Because of different backgrounds, values, and personalities, those surveyed may evaluate the same situation differently. Caution should thus be used when interpreting perception- based indicators. Nevertheless, they convey much needed information on trade facilitation.", "origins": [ { "producer": "Review of Maritime Transport - UNCTAD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Review of Maritime Transport 2010., UN Conference on Trade and Development (UNCTAD). Indicator IS.SHP.GCNW.XQ (https://data.worldbank.org/indicator/IS.SHP.GCNW.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IS.SHP.GCNW.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "maximum value in 2004 = 100", "display": { "unit": "maximum value in 2004 = 100" } }, "is_shp_good_tu": { "title": "Container port traffic (TEU: 20 foot equivalent units)", "description_from_producer": "Port container traffic measures the flow of containers from land to sea transport modes, and vice versa, in twenty-foot equivalent units (TEUs), a standard-size container. Data refer to coastal shipping as well as international journeys. Transshipment traffic is counted as two lifts at the intermediate port (once to off-load and again as an outbound lift) and includes empty units.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: TEU is the standard unit, referring to 20-foot equivalent units or 20-foot-long cargo container. The size of cargo containers range from 20 feet long to more than 50 feet long. The international measure is the smallest box, the 20-footer or 20-foot-equivalent unit (TEU). Two twenty-foot containers (TEUs) equal one FEU. Container vessel capacity and port throughput capacity are frequently referred to in TEUs.\n\nFor any questions related to the country data and methodology, please contact the Review of Maritime Transport team at: unctad-rmt@un.org\n\n### Development relevance:\nTransport infrastructure - highways, railways, ports and waterways, and airports and air traffic control systems - and the services that flow from it are crucial to the activities of households, producers, and governments. Because performance indicators vary widely by transport mode and focus (whether physical infrastructure or the services flowing from that infrastructure), highly specialized and carefully specified indicators are required to measure a country's transport infrastructure.\n\nThe sea transport industry a vital engine of global socio-economic growth. It is of vital importance for economic development, creating direct and indirect employment, supporting tourism and local businesses, and stimulating foreign investment and international trade. Economic growth, technological change, market liberalization, and oil prices affect sea transport throughout the world.\n\n### Limitations and exceptions:\nMeasures of port container traffic, much of it commodities of medium to high value added, give some indication of economic growth in a country. But when traffic is merely transshipment, much of the economic benefit goes to the terminal operator and ancillary services for ships and containers rather than to the country more broadly. In transshipment centers empty containers may account for as much as 40 percent of traffic.\n\nData cover coastal shipping as well as international journeys. Transshipment traffic is counted as two lifts at the intermediate port (once to off-load and again as an outbound lift) and includes empty units. Data for transport sectors are not always internationally comparable. Unlike for demographic statistics, national income accounts, and international trade data, the collection of infrastructure data has not been \"internationalized.\"", "origins": [ { "producer": "UN Conference on Trade and Development (UNCTAD), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Conference on Trade and Development (UNCTAD), uri: http://unctad.org/en/Pages/statistics.aspx. Indicator IS.SHP.GOOD.TU (https://data.worldbank.org/indicator/IS.SHP.GOOD.TU). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IS.SHP.GOOD.TU", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "TEU: 20 foot equivalent units", "display": { "unit": "TEU: 20 foot equivalent units" } }, "it_cel_sets": { "title": "Mobile cellular subscriptions", "description_from_producer": "Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Refers to the subscriptions to a public mobile telephone service and provides access to Public Switched Telephone Network (PSTN) using cellular technology, including number of pre-paid SIM cards active during the past three months. This includes both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services should also be excluded. This should include all mobile cellular subscriptions that offer voice communications.\n\nData on mobile cellular subscribers are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected from all licensed mobile-cellular operators in the country, and then aggregated at the country level. If retail mobile-cellular services are also provided by nonfacilities-based operators (i.e., mobile virtual network operators), care should be taken to avoid double counting. One difficulty that may arise is that operators may have different definitions of \u2018active\u2019 and therefore may not be able to provide the data according to the recommended definition (i.e., used in the last three months).\n\n### Development relevance:\nThe quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. The International Telecommunication Union (ITU) estimates that there were about 6 billion mobile subscriptions globally in the early 2010s. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nMobile cellular telephone subscriptions are subscriptions to a public mobile telephone service using cellular technology, which provide access to the public switched telephone network (PSTN) using cellular technology. It includes postpaid and prepaid subscriptions and includes analogue and digital cellular systems.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency.\n\n### Limitations and exceptions:\nOperators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.\n\nMobile subscriptions include both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services are also excluded, but all mobile cellular subscriptions that offer voice communications are included. Both postpaid and prepaid subscriptions are included.\n\n### Other notes:\nPlease cite the International Telecommunication Union for third-party use of these data.", "origins": [ { "producer": "International Telecommunication Union (ITU), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU). Indicator IT.CEL.SETS (https://data.worldbank.org/indicator/IT.CEL.SETS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IT.CEL.SETS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "", "display": { "numDecimalPlaces": 0 } }, "it_cel_sets_p2": { "title": "Mobile cellular subscriptions (per 100 people)", "description_from_producer": "Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provide access to the PSTN using cellular technology. The indicator includes (and is split into) the number of postpaid subscriptions, and the number of active prepaid accounts (i.e. that have been used during the last three months). The indicator applies to all mobile cellular subscriptions that offer voice communications. It excludes subscriptions via data cards or USB modems, subscriptions to public mobile data services, private trunked mobile radio, telepoint, radio paging and telemetry services.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Refers to the subscriptions to a public mobile telephone service and provides access to Public Switched Telephone Network (PSTN) using cellular technology, including number of pre-paid SIM cards active during the past three months. This includes both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services should also be excluded. This should include all mobile cellular subscriptions that offer voice communications.\n\nData on mobile cellular subscribers are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports.\n\nMobile cellular subscriptions (per 100 people) indicator is derived by all mobile subscriptions divided by the country's population and multiplied by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected from all licensed mobile-cellular operators in the country, and then aggregated at the country level. If retail mobile-cellular services are also provided by nonfacilities-based operators (i.e., mobile virtual network operators), care should be taken to avoid double counting. One difficulty that may arise is that operators may have different definitions of \u2018active\u2019 and therefore may not be able to provide the data according to the recommended definition (i.e., used in the last three months). This indicator can be divided by the population and multiplied by 100 to obtain mobile cellular subscriptions per 100 people.\n\n### Development relevance:\nThe quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. The International Telecommunication Union (ITU) estimates that there were about 6 billion mobile subscriptions globally in the early 2010s. No technology has ever spread faster around the world. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nMobile cellular telephone subscriptions are subscriptions to a public mobile telephone service using cellular technology, which provide access to the public switched telephone network (PSTN) using cellular technology. It includes postpaid and prepaid subscriptions and includes analogue and digital cellular systems.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency.\n\n### Limitations and exceptions:\nOperators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.\n\nMobile subscriptions include both analogue and digital cellular systems (IMT-2000 (Third Generation, 3G) and 4G subscriptions, but excludes mobile broadband subscriptions via data cards or USB modems. Subscriptions to public mobile data services, private trunked mobile radio, telepoint or radio paging, and telemetry services are also excluded, but all mobile cellular subscriptions that offer voice communications are included. Both postpaid and prepaid subscriptions are included.\n\n### Other notes:\nPlease cite the International Telecommunication Union for third-party use of these data.", "origins": [ { "producer": "International Telecommunication Union (ITU), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU). Indicator IT.CEL.SETS.P2 (https://data.worldbank.org/indicator/IT.CEL.SETS.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IT.CEL.SETS.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100 people", "display": { "unit": "per 100 people" } }, "it_mlt_main": { "title": "Fixed telephone subscriptions", "description_from_producer": "Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: A fixed telephone line (previously called main telephone line in operation) is an active line connecting the subscriber's terminal equipment to the public switched telephone network (PSTN) and which has a dedicated port in the telephone exchange equipment. This term is synonymous with the terms main station or Direct Exchange Line (DEL) that are commonly used in telecommunication documents. It may not be the same as an access line or a subscriber. This should include the active number of analog fixed telephone lines, ISDN channels, fixed wireless, public payphones and VoIP subscriptions. Active lines are those that have registered an activity in the past three months.\n\nData on fixed telephone lines are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators. Data are considered to be very reliable, timely, and complete.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected and aggregated at the country level by asking all licensed fixed-telephone line operators how many fixed-telephone subscriptions they have.\n\n### Development relevance:\nThe quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout.\n\nFixed telephone lines are those that connect a subscriber's terminal equipment to the public switched telephone network and that have a port on a telephone exchange. This term is synonymous with the term main station or Direct Exchange Line (DEL) that is commonly used in telecommunication documents. Integrated services digital network channels and fixed wireless subscribers are included. A fixed line also refers to a phone which uses a solid medium telephone line such as a metal wire or fiber optic cable for transmission as distinguished from a mobile cellular line which uses radio waves for transmission.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency.\n\n### Limitations and exceptions:\nOperators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.\n\n### Other notes:\nPlease cite the International Telecommunication Union for third-party use of these data.", "origins": [ { "producer": "International Telecommunication Union (ITU), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU). Indicator IT.MLT.MAIN (https://data.worldbank.org/indicator/IT.MLT.MAIN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IT.MLT.MAIN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "it_mlt_main_p2": { "title": "Fixed telephone subscriptions (per 100 people)", "description_from_producer": "Fixed telephone subscriptions refers to the sum of active number of analogue fixed telephone lines, voice-over-IP (VoIP) subscriptions, fixed wireless local loop (WLL) subscriptions, ISDN voice-channel equivalents and fixed public payphones.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: A fixed telephone line (previously called main telephone line in operation) is an active line connecting the subscriber's terminal equipment to the public switched telephone network (PSTN) and which has a dedicated port in the telephone exchange equipment. This term is synonymous with the terms main station or Direct Exchange Line (DEL) that are commonly used in telecommunication documents. It may not be the same as an access line or a subscriber. This should include the active number of analog fixed telephone lines, ISDN channels, fixed wireless, public payphones and VoIP subscriptions. Active lines are those that have registered an activity in the past three months.\n\nData on fixed telephone lines are derived using administrative data that countries (usually the regulatory telecommunication authority or the Ministry in charge of telecommunications) regularly, and at least annually, collect from telecommunications operators. Data are considered to be very reliable, timely, and complete.\n\nData for this indicator are readily available for approximately 90 percent of countries, either through ITU's World Telecommunication Indicators questionnaires or from official information available on the Ministry or Regulator's website. For the rest, information can be aggregated through operators' data (mainly through annual reports) and complemented by market research reports.\n\nTelephone lines (per 100 people) indicator is derived by all telephone lines divided by the country's population and multiplied by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): Data can be collected and aggregated at the country level by asking all licensed fixed-telephone line operators how many fixed-telephone subscriptions they have. This indicator can be divided by the population and multiplied by 100 to obtain fixed telephone subscriptions per 100 people.\n\n### Development relevance:\nThe quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout.\n\nFixed telephone lines are those that connect a subscriber's terminal equipment to the public switched telephone network and that have a port on a telephone exchange. This term is synonymous with the term main station or Direct Exchange Line (DEL) that is commonly used in telecommunication documents. Integrated services digital network channels and fixed wireless subscribers are included. A fixed line also refers to a phone which uses a solid medium telephone line such as a metal wire or fiber optic cable for transmission as distinguished from a mobile cellular line which uses radio waves for transmission.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency.\n\n### Limitations and exceptions:\nOperators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. For example, some countries do not include the number of ISDN channels when calculating the number of fixed telephone lines. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year. Data are usually not adjusted but discrepancies in the definition, reference year or the break in comparability in between years are noted in a data note. For this reason, data are not always strictly comparable. Missing values are estimated by ITU.\n\n### Other notes:\nPlease cite the International Telecommunication Union for third-party use of these data.", "origins": [ { "producer": "International Telecommunication Union (ITU), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU). Indicator IT.MLT.MAIN.P2 (https://data.worldbank.org/indicator/IT.MLT.MAIN.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IT.MLT.MAIN.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100 people", "display": { "unit": "per 100 people" } }, "it_net_bbnd": { "title": "Fixed broadband subscriptions", "description_from_producer": "Fixed broadband subscriptions refers to fixed subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fiber-to-the-home/building, other fixed (wired)-broadband subscriptions, satellite broadband and terrestrial fixed wireless broadband. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It should include fixed WiMAX and any other fixed wireless technologies. It includes both residential subscriptions and subscriptions for organizations.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The data can be collected by asking all ISPs in the country to provide the number of their fixed -broadband subscriptions (by type \u2013 cable, DSL, optical fiber, other, satellite, and terrestrial fixed wireless broadband).\nStatistical concept(s): The data can be collected by asking all ISPs in the country to provide the number of their fixed -broadband subscriptions (by type \u2013 cable, DSL, optical fiber, other, satellite, and terrestrial fixed wireless broadband).\n\n### Other notes:\nPlease cite the International Telecommunication Union for third-party use of these data.", "origins": [ { "producer": "International Telecommunication Union (ITU), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU). Indicator IT.NET.BBND (https://data.worldbank.org/indicator/IT.NET.BBND). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IT.NET.BBND", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "it_net_bbnd_p2": { "title": "Fixed broadband subscriptions (per 100 people)", "description_from_producer": "Fixed broadband subscriptions refers to fixed subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fiber-to-the-home/building, other fixed (wired)-broadband subscriptions, satellite broadband and terrestrial fixed wireless broadband. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It should include fixed WiMAX and any other fixed wireless technologies. It includes both residential subscriptions and subscriptions for organizations.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data refer to subscriptions to high-speed access to the public Internet (a TCP/IP connection), at downstream speeds equal to, or greater than, 256 kbit/s. This includes cable modem, DSL, fibre-to-the-home/building and other fixed (wired)-broadband subscriptions. This total is measured irrespective of the method of payment. It excludes subscriptions that have access to data communications (including the Internet) via mobile-cellular networks. It excludes technologies listed under the wireless-broadband category.\n\nFixed broadband Internet subscribers per 100 people is obtained by dividing the number of fixed broadband Internet subscribers by the population and then multiplying by 100. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\nStatistical concept(s): The data can be collected by asking all ISPs in the country to provide the number of their fixed -broadband subscriptions (by type \u2013 cable, DSL, optical fiber, other, satellite, and terrestrial fixed wireless broadband). This indicator can be divided by the population and multiplied by 100 to obtain fixed-broadband subscriptions per 100 people.\n\n### Development relevance:\nThe quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Government effort alone is not enough to meet the need for investments in modern infrastructure; public-private partnerships, especially those involving local providers and financiers, are critical for lowering costs and delivering value for money. In telecommunications, competition in the marketplace, along with sound regulation, is lowering costs, improving quality, and easing access to services around the globe.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains.\n\nThere are several economic gains associated with broadband. For example, with DSL, users can use a single standard phone line for both voice and data services. This enables them to surf the Internet and call a friend at the same time - all using the same phone line. Broadband also enhances many Internet applications such as new e-government services like electronic tax filing, online health care services, e-learning and increased levels of electronic commerce.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Mobile communications have a particularly important impact in rural areas. The mobility, ease of use, flexible deployment, and relatively low and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. The next billion mobile subscribers will consist mainly of the rural poor. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met.\n\nOver the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency.\n\n### Limitations and exceptions:\nData are collected by national statistics offices through household surveys. Because survey questions and definitions differ, the estimates may not be strictly comparable across countries.\n\nFixed broadband Internet includes cable modem, DSL, fibre and other fixed broadband technology (such as satellite broadband Internet, Ethernet LANs, fixed-wireless access, Wireless Local Area Network, WiMAX etc.). Subscribers with access to data communications (including the Internet) via mobile cellular networks are excluded.\n\nAdvertised and real speeds can differ substantially. In some countries, regulatory authorities monitor the speed and quality of broadband services and oblige operators to provide accurate quality-of-service information to end users. Regional and global totals are calculated as unweighted sums of the country values. Regional and global penetration rates (per 100 inhabitants) are weighted averages of the country values weighted by the population of the countries/regions.\n\nDiscrepancies between global and national figures may arise when countries use a different definition than the one used by ITU. Discrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is end of December of every year. A number of countries have fiscal years that end in March or June of every year.\n\n### Other notes:\nPlease cite the International Telecommunication Union for third-party use of these data.", "origins": [ { "producer": "International Telecommunication Union (ITU), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU). Indicator IT.NET.BBND.P2 (https://data.worldbank.org/indicator/IT.NET.BBND.P2). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IT.NET.BBND.P2", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100 people", "display": { "unit": "per 100 people" } }, "it_net_secr": { "title": "Secure Internet servers", "description_from_producer": "The number of distinct, publicly-trusted TLS/SSL certificates found in the Netcraft Secure Server Survey (by hosting country).\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Netcraft's survey counts (unique) valid certificates issued by widely-trusted third-party certification authorities. A certificate must be valid, that is it must be within its validity period (certificates are usually valid for up to 39 months), and the digital signatures on the certificate must check successfully. It must be issued by third party certificate issuer that is recognised by Netcraft.\nNetcraft gathers a list of possible SSL web sites to investigate from a range of different sources. The data reflects the December survey in that year.\n\nSSL (Secure Socket Layer) is a protocol developed by Netscape for encrypted transmission over TCP/IP networks. It sets up a secure end-to-end link over which HTTP or any other application protocol can operate. The most common application of SSL is HTTPS for SSL-encrypted HTTP. It has now been replaced with a IETF-standardised version, TLS.\n\nSurveying: Netcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behavior is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority. The survey makes multiple HTTPS request types to identify both web server capabilities, and the certificates in use. Netcraft makes retry visits to any non-responding IP addresses once, several hours after the failed visits. The information made available by an HTTPS server is more substantial than with http servers. The most interesting piece of information available from HTTP servers is the server signature; this can be analyzed to give straightforward empirical evidence about the relative popularity of server software on web sites across the Internet. The same information is also available from HTTPS servers. Additionally, the content of the site's X.509 certificate is available, providing details about both the company or organization owning the site, and the certificate issuer. Furthermore, in most cases the TCP/IP characteristics of the network connection allows to determine the operating system used.\n\nStatistical concept(s): The survey examines the use of encrypted transactions through extensive automated exploration, tallying the number of web sites using HTTPS. This analysis relates to those sites found in the survey where the certificate is valid for the hostname, and the certificate has been issued from a publicly-trusted root. The indicator refers to valid, third-party certificates. Included are sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed. The location is derived from the hosting location of the sites using the certificates (rather than the countries indicated on the certificates themselves.) This analysis relates to those sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed.\n\n### Development relevance:\nThe quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Comparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met. Over the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency.\n\n### Limitations and exceptions:\nNetcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behaviour is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority.", "origins": [ { "producer": "Netcraft Secure Server Survey, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Secure Server Survey, Netcraft, uri: http://www.netcraft.com/. Indicator IT.NET.SECR (https://data.worldbank.org/indicator/IT.NET.SECR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IT.NET.SECR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "it_net_secr_p6": { "title": "Secure Internet servers (per 1 million people)", "description_from_producer": "The number of distinct, publicly-trusted TLS/SSL certificates found in the Netcraft Secure Server Survey (by hosting country), per 1 million people.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Netcraft's survey counts (unique) valid certificates issued by widely-trusted third-party certification authorities. A certificate must be valid, that is it must be within its validity period (certificates are usually valid for up to 39 months), and the digital signatures on the certificate must check successfully. It must be issued by third party certificate issuer that is recognised by Netcraft.\nNetcraft gathers a list of possible SSL web sites to investigate from a range of different sources. The data reflects the December survey in that year.\n\nSSL (Secure Socket Layer) is a protocol developed by Netscape for encrypted transmission over TCP/IP networks. It sets up a secure end-to-end link over which HTTP or any other application protocol can operate. The most common application of SSL is HTTPS for SSL-encrypted HTTP. It has now been replaced with a IETF-standardised version, TLS.\n\nSurveying: Netcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behavior is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority. The survey makes multiple HTTPS request types to identify both web server capabilities, and the certificates in use. Netcraft makes retry visits to any non-responding IP addresses once, several hours after the failed visits. The information made available by an HTTPS server is more substantial than with http servers. The most interesting piece of information available from HTTP servers is the server signature; this can be analyzed to give straightforward empirical evidence about the relative popularity of server software on web sites across the Internet. The same information is also available from HTTPS servers. Additionally, the content of the site's X.509 certificate is available, providing details about both the company or organization owning the site, and the certificate issuer. Furthermore, in most cases the TCP/IP characteristics of the network connection allows to determine the operating system used.\nStatistical concept(s): The survey examines the use of encrypted transactions through extensive automated exploration, tallying the number of web sites using HTTPS. This analysis relates to those sites found in the survey where the certificate is valid for the hostname, and the certificate has been issued from a publicly-trusted root. The indicator refers to valid, third-party certificates. Included are sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed. The location is derived from the hosting location of the sites using the certificates (rather than the countries indicated on the certificates themselves.) This analysis relates to those sites found in the survey where the common name in the certificate matched the hostname, and the certificate's digital signature was not detected as being self-signed.\n\n### Development relevance:\nThe quality of an economy's infrastructure, including power and communications, is an important element in investment decisions for both domestic and foreign investors. Comparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses.\n\nAccess to telecommunication services rose on an unprecedented scale over the past two decades. This growth was driven primarily by wireless technologies and liberalization of telecommunications markets, which have enabled faster and less costly network rollout. Access is the key to delivering telecommunications services to people. If the service is not affordable to most people, goals of universal usage will not be met. Over the past decade new financing and technology, along with privatization and market liberalization, have spurred dramatic growth in telecommunications in many countries. With the rapid development of mobile telephony and the global expansion of the Internet, information and communication technologies are increasingly recognized as essential tools of development, contributing to global integration and enhancing public sector effectiveness, efficiency, and transparency.\n\n### Limitations and exceptions:\nNetcraft only visits sites on the standard HTTPS port, 443. Whilst it is possible to run an HTTPS server on a different port (using a URL like https://example.com:7000) this behaviour is quite rare on public websites. For example, usual ports are often used for administrative interfaces and other services not intended for the general public.\n\nNetcraft tries to visit every public secure website. Note that the survey does not include secure mail servers (SMTP) or intranet sites. So the final number of certificates for each certificate authority will be lower than the total number of server certificates sold by that authority.\n\n### Other notes:\nThis indicator measures the number of secure internet servers per 1 million people in a country, adjusting for population size to allow cross-country comparisons. Please note that countries with populations below 1 million may exhibit disproportionately high values, as the smaller denominator can inflate the per-capita figure. For instance, a country with 50,000 people and 500 secure servers would yield a ratio of 10,000 servers per 1 million people, despite the modest absolute number. For a balanced perspective, it is advisable to consider both the per-capita ratio and the absolute number of servers.", "origins": [ { "producer": "Netcraft Secure Server Survey and World Bank population estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Secure Server Survey, Netcraft, uri: http://www.netcraft.com/;\nWorld Bank population estimates, World Bank (WB). Indicator IT.NET.SECR.P6 (https://data.worldbank.org/indicator/IT.NET.SECR.P6). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IT.NET.SECR.P6", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1 million people", "display": { "unit": "per 1 million people" } }, "it_net_user_fe_zs": { "title": "Individuals using the Internet, female (% of female population)", "description_from_producer": "This indicator refers to female individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\n\n### Development relevance:\nThe digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains.\n\n### Limitations and exceptions:\nOperators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year.\n\n### Other notes:\nPlease cite the International Telecommunication Union for third-party use of these data.", "origins": [ { "producer": "International Telecommunication Union (ITU), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU). Indicator IT.NET.USER.FE.ZS (https://data.worldbank.org/indicator/IT.NET.USER.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IT.NET.USER.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "it_net_user_ma_zs": { "title": "Individuals using the Internet, male (% of male population)", "description_from_producer": "This indicator refers to male individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\n\n### Development relevance:\nThe digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nToday's smartphones and tablets have computer power equivalent to that of yesterday's computers and provide a similar range of functions. Device convergence is thus rendering the conventional definition obsolete.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. However, despite significant improvements in the developing world, the gap between the ICT haves and have-nots remains.\n\n### Limitations and exceptions:\nOperators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\n\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year.\n\n### Other notes:\nPlease cite the International Telecommunication Union for third-party use of these data.", "origins": [ { "producer": "International Telecommunication Union (ITU), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU). Indicator IT.NET.USER.MA.ZS (https://data.worldbank.org/indicator/IT.NET.USER.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IT.NET.USER.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "it_net_user_zs": { "title": "Individuals using the Internet (% of population)", "description_short": "Share of the population who used the Internet in the last three months.", "description_from_producer": "Internet users are individuals who have used the Internet (from any location) in the last 3 months. The Internet can be used via a computer, mobile phone, personal digital assistant, games machine, digital TV etc.\nLimitations and exceptions: Operators have traditionally been the main source of telecommunications data, so information on subscriptions has been widely available for most countries. This gives a general idea of access, but a more precise measure is the penetration rate - the share of households with access to telecommunications. During the past few years more information on information and communication technology use has become available from household and business surveys. Also important are data on actual use of telecommunications services. Ideally, statistics on telecommunications (and other information and communications technologies) should be compiled for all three measures: subscriptions, access, and use. The quality of data varies among reporting countries as a result of differences in regulations covering data provision and availability.\nDiscrepancies may also arise in cases where the end of a fiscal year differs from that used by ITU, which is the end of December of every year. A number of countries have fiscal years that end in March or June of every year.\nStatistical concept and methodology: The Internet is a world-wide public computer network. It provides access to a number of communication services including the World Wide Web and carries email, news, entertainment and data files, irrespective of the device used (not assumed to be only via a computer - it may also be by mobile phone, PDA, games machine, digital TV etc.). Access can be via a fixed or mobile network. For additional/latest information on sources and country notes, please also refer to: https://www.itu.int/en/ITU-D/Statistics/Pages/stat/default.aspx\n[Text from [World Bank World Development Indicators metadata](https://databank.worldbank.org/metadataglossary/world-development-indicators/series/IT.NET.USER.ZS)]", "description_key": "- An Internet user is defined by the International Telecommunication Union as anyone who has accessed the Internet from any location in the last three months.\n- This can be from any type of device, including a computer, mobile phone, personal digital assistant, games machine, digital TV, and other technological devices.", "origins": [ { "producer": "World Telecommunication/ICT Indicators Database - International Telecommunication Union (ITU), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Telecommunication/ICT Indicators Database, International Telecommunication Union (ITU), uri: https://datahub.itu.int/. Indicator IT.NET.USER.ZS (https://data.worldbank.org/indicator/IT.NET.USER.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IT.NET.USER.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "% of population" }, "processing_level": "minor", "presentation": { "grapher_config": { "title": "Share of the population using the Internet", "subtitle": "Share of the population who [used the Internet](#dod:internetuser) in the last three months.", "timelineMinTime": 1990, "originUrl": "https://ourworldindata.org/internet", "hideAnnotationFieldsInTitle": { "time": true, "entity": true, "changeInPrefix": true }, "hasMapTab": true, "yAxis": { "min": 0, "max": 100 }, "map": { "timeTolerance": 5, "colorScale": { "baseColorScheme": "Blues", "colorSchemeInvert": false, "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 ], "customNumericLabels": [ "", "", "", "", "", "", "", "", "", "" ], "legendDescription": "Individuals using the Internet (% of population)" } }, "selectedEntityNames": [ "South Asia (WB)", "North America (WB)", "Sub-Saharan Africa (WB)", "East Asia and Pacific (WB)", "Middle East, North Africa, Afghanistan and Pakistan (WB)", "Europe and Central Asia (WB)", "Latin America and Caribbean (WB)", "World" ] }, "title_public": "Share of the population using the Internet", "title_variant": "International Telecommunication Union", "topic_tags": [ "Internet", "Technological Change", "Loneliness & Social Connections" ] } }, "lp_exp_durs_md": { "title": "Lead time to export, median case (days)", "description_from_producer": "Lead time to export is the median time (the value for 50 percent of shipments) from shipment point to port of loading. Data are from the Logistics Performance Index survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments). The data are exponentiated averages of the logarithm of single value responses and of midpoint values of range responses for the median case.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Data on lead time to export are from the Logistics Performance Index (LPI) survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments) of shipments from the point of origin (the seller's factory, typically located either in the capital city or in the largest commercial center) to the port of loading or equivalent (port/airport), and excluding international shipping.\n\nThe Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and at the main express carriers. The 2012 LPI data are based on the 2011 survey, which was administered to nearly 1,000 respondents at international logistics companies in 143 countries (domestic performance indicators). The international LPI covers 155 countries. The LPI assesses both large companies and small and medium enterprises. Most of the responses are from small and medium enterprises, with large companies (those with 250 employees or more) accounting for roughly 18 percent of responses. The respondents include groups of professionals who are directly involved in day-today operations, from company headquarters and from country offices such as senior executives, area or country managers, and department managers. Many of the respondents are at corporate or regional headquarters or at country branch offices. The rest are at local branch offices or independent firms. The majority of respondents are involved in providing most logistics services as their main line of work such as warehousing and distribution, customer-tailored logistics solutions, courier services, bulk or break bulk cargo transport, and less-than-full container, full-container, or full-trailer load transport.\n\nFor the lead time to export, respondents were asked for quantitative information on their countries' international supply chains by picking choices from a dropdown menu. When a response indicates a single value, the answer is coded as the logarithm of that value. When a response indicates a range, the answer is coded as the logarithm of the midpoint of that range. Country scores are produced by exponentiating the average of responses in logarithms across all respondents for a given country. This method is equivalent to taking a geometric average in levels. Scores for regions, income groups, and LPI quintiles are simple averages of the relevant country scores.\n\n### Development relevance:\nThe LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce.\n\nA useful outcome measure of logistics performance is the time taken to complete trade transactions. The median import lead time for port and airport supply chains, as measured for the LPI, is more than 3.5 times longer in low performing countries than in high-performing countries. The difference is around three times for land supply chains. The association suggests that geographical hurdles, and perhaps internal transport markets, still pose substantial difficulties in many countries. Besides geography and speed en route, another factor in import lead times is the border process. Time can be reduced at all stages of this process, but especially in the clearance of goods on arrival. Countries with low logistics performance need to reform their border management so that they can reduce red tape, excessive and opaque procedural requirements, and physical inspections. Although the time to clear goods through customs is a fairly small fraction of total import time for all LPI quintiles, it rises sharply if goods are physically inspected. Core customs procedures are similar across quintiles. But low-performing countries have a far higher prevalence of physical inspection, even subjecting the same shipment to repeated inspections by multiple agencies.\n\nMany low-income countries have long export lead times, reducing their export competitiveness and ability to participate in international trade.\n\n### Limitations and exceptions:\nThe Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Feedback from operators is supplemented with quantitative data on the performance of key components of the logistics chain in the country of work. Thus, the LPI consists of both qualitative and quantitative measures.\n\nIn addition, despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Logistics Performance Index - World Bank and Turku School of Economics", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Logistic Performance Index Surveys, World Bank (WB), uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.;\nTurku School of Economics, uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report. Indicator LP.EXP.DURS.MD (https://data.worldbank.org/indicator/LP.EXP.DURS.MD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/LP.EXP.DURS.MD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "days", "short_unit": "days", "display": { "shortUnit": "days", "unit": "days" } }, "lp_imp_durs_md": { "title": "Lead time to import, median case (days)", "description_from_producer": "Lead time to import is the median time (the value for 50 percent of shipments) from port of discharge to arrival at the consignee. Data are from the Logistics Performance Index survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments). The data are exponentiated averages of the logarithm of single value responses and of midpoint values of range responses for the median case.\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: Data on lead time to import are from the Logistics Performance Index (LPI) survey. Respondents provided separate values for the best case (10 percent of shipments) and the median case (50 percent of shipments) of shipments from the port of discharge or equivalent to the buyer's warehouse.\n\nThe Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and at the main express carriers. The 2012 LPI data are based on the 2011 survey, which was administered to nearly 1,000 respondents at international logistics companies in 143 countries (domestic performance indicators). The international LPI covers 155 countries. The LPI assesses both large companies and small and medium enterprises. Most of the responses are from small and medium enterprises, with large companies (those with 250 employees or more) accounting for roughly 18 percent of responses. The respondents include groups of professionals who are directly involved in day-today operations, from company headquarters and from country offices such as senior executives, area or country managers, and department managers. Many of the respondents are at corporate or regional headquarters or at country branch offices. The rest are at local branch offices or independent firms. The majority of respondents are involved in providing most logistics services as their main line of work such as warehousing and distribution, customer-tailored logistics solutions, courier services, bulk or break bulk cargo transport, and less-than-full container, full-container, or full-trailer load transport.\n\nFor the lead time to import, respondents were asked for quantitative information on their countries' international supply chains by picking choices from a dropdown menu. When a response indicates a single value, the answer is coded as the logarithm of that value. When a response indicates a range, the answer is coded as the logarithm of the midpoint of that range. Country scores are produced by exponentiating the average of responses in logarithms across all respondents for a given country. This method is equivalent to taking a geometric average in levels. Scores for regions, income groups, and LPI quintiles are simple averages of the relevant country scores.\n\n### Development relevance:\nThe LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce.\n\nA useful outcome measure of logistics performance is the time taken to complete trade transactions. The median import lead time for port and airport supply chains, as measured for the LPI, is more than 3.5 times longer in low performing countries than in high-performing countries. The difference is around three times for land supply chains. The association suggests that geographical hurdles, and perhaps internal transport markets, still pose substantial difficulties in many countries. Besides geography and speed en route, another factor in import lead times is the border process. Time can be reduced at all stages of this process, but especially in the clearance of goods on arrival. Countries with low logistics performance need to reform their border management so that they can reduce red tape, excessive and opaque procedural requirements, and physical inspections. Although the time to clear goods through customs is a fairly small fraction of total import time for all LPI quintiles, it rises sharply if goods are physically inspected. Core customs procedures are similar across quintiles. But low-performing countries have a far higher prevalence of physical inspection, even subjecting the same shipment to repeated inspections by multiple agencies.\n\nMany low-income countries have long export lead times, reducing their export competitiveness and ability to participate in international trade.\n\n### Limitations and exceptions:\nThe Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Feedback from operators is supplemented with quantitative data on the performance of key components of the logistics chain in the country of work. Thus, the LPI consists of both qualitative and quantitative measures.\n\nIn addition, despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms.\n\n### Other notes:\nFor aggregate data, each economy is classified based on the classification of World Bank Group's fiscal year 2026 (July 1, 2024-June 30, 2026).", "origins": [ { "producer": "Logistics Performance Index - World Bank and Turku School of Economics", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Logistic Performance Index Surveys, World Bank (WB), uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report.;\nTurku School of Economics, uri: https://lpi.worldbank.org/, note: Summary results are published in Arvis and others' Connecting to Compete: Trade Logistics in the Global Economy, The Logistics Performance Index and Its Indicators report. Indicator LP.IMP.DURS.MD (https://data.worldbank.org/indicator/LP.IMP.DURS.MD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/LP.IMP.DURS.MD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "days", "short_unit": "days", "display": { "shortUnit": "days", "unit": "days" } }, "lp_lpi_cust_xq": { "title": "Logistics performance index: Efficiency of customs clearance process (1=low to 5=high)", "description_from_producer": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated efficiency of customs clearance processes (i.e. speed, simplicity and predictability of formalities), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.\n\n### Periodicity:\nEvery few years\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals.\n\n### Development relevance:\nThe LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI.\n\n### Limitations and exceptions:\nThe Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms.\n\n### Other notes:\nThis indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/.", "origins": [ { "producer": "Logistics Performance Index (LPI) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org. Indicator LP.LPI.CUST.XQ (https://data.worldbank.org/indicator/LP.LPI.CUST.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/LP.LPI.CUST.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 5=high", "display": { "unit": "1=low to 5=high" } }, "lp_lpi_infr_xq": { "title": "Logistics performance index: Quality of trade and transport-related infrastructure (1=low to 5=high)", "description_from_producer": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated the quality of trade and transport related infrastructure (e.g. ports, railroads, roads, information technology), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.\n\n### Periodicity:\nEvery few years\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals.\n\n### Development relevance:\nThe LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI.\n\n### Limitations and exceptions:\nThe Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms.\n\n### Other notes:\nThis indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/.", "origins": [ { "producer": "Logistics Performance Index (LPI) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org. Indicator LP.LPI.INFR.XQ (https://data.worldbank.org/indicator/LP.LPI.INFR.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/LP.LPI.INFR.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 5=high", "display": { "unit": "1=low to 5=high" } }, "lp_lpi_itrn_xq": { "title": "Logistics performance index: Ease of arranging competitively priced shipments (1=low to 5=high)", "description_from_producer": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents assessed the ease of arranging competitively priced shipments to markets, on a rating ranging from 1 (very difficult) to 5 (very easy). Scores are averaged across all respondents.\n\n### Periodicity:\nEvery few years\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency with which shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores for the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals.\n\n### Development relevance:\nThe LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI.\n\n### Limitations and exceptions:\nThe Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms.\n\n### Other notes:\nThis indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/.", "origins": [ { "producer": "Logistics Performance Index (LPI) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org. Indicator LP.LPI.ITRN.XQ (https://data.worldbank.org/indicator/LP.LPI.ITRN.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/LP.LPI.ITRN.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 5=high", "display": { "unit": "1=low to 5=high" } }, "lp_lpi_logs_xq": { "title": "Logistics performance index: Competence and quality of logistics services (1=low to 5=high)", "description_from_producer": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated the overall level of competence and quality of logistics services (e.g. transport operators, customs brokers), on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.\n\n### Periodicity:\nEvery few years\n\n### Statistical concept and methodology:\nMethodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency that shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores for the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals.\n\n### Development relevance:\nThe LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI.\n\n### Limitations and exceptions:\nThe Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms.\n\n### Other notes:\nThis indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/.", "origins": [ { "producer": "Logistics Performance Index (LPI) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org. Indicator LP.LPI.LOGS.XQ (https://data.worldbank.org/indicator/LP.LPI.LOGS.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/LP.LPI.LOGS.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 5=high", "display": { "unit": "1=low to 5=high" } }, "lp_lpi_ovrl_xq": { "title": "Logistics performance index: Overall (1=low to 5=high)", "description_from_producer": "The Logistics Performance Index overall score reflects perceptions of a country's logistics based on the efficiency of customs clearance process, quality of trade- and transport-related infrastructure, ease of arranging competitively priced shipments, quality of logistics services, ability to track and trace consignments, and frequency with which shipments reach the consignee within the scheduled time. The index ranges from 1 to 5, with a higher score representing better performance.\n\nData are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets.\n\nScores for the six areas are averaged across all respondents and aggregated to a single score using principal components analysis.\n\nDetails of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report.\n\n### Periodicity:\nEvery few years\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals.\n\n### Development relevance:\nThe LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI.\n\n### Limitations and exceptions:\nThe Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms.\n\n### Other notes:\nThis indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/.", "origins": [ { "producer": "Logistics Performance Index (LPI) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org. Indicator LP.LPI.OVRL.XQ (https://data.worldbank.org/indicator/LP.LPI.OVRL.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/LP.LPI.OVRL.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 5=high", "display": { "unit": "1=low to 5=high" } }, "lp_lpi_time_xq": { "title": "Logistics performance index: Frequency with which shipments reach consignee within scheduled or expected time (1=low to 5=high)", "description_from_producer": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents assessed how often the shipments to assessed markets reach the consignee within the scheduled or expected delivery time, on a rating ranging from 1 (hardly ever) to 5 (nearly always). Scores are averaged across all respondents.\n\n### Periodicity:\nEvery few years\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores for the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals.\n\n### Development relevance:\nThe LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI.\n\n### Limitations and exceptions:\nThe Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms.\n\n### Other notes:\nThis indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/.", "origins": [ { "producer": "Logistics Performance Index (LPI) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org. Indicator LP.LPI.TIME.XQ (https://data.worldbank.org/indicator/LP.LPI.TIME.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/LP.LPI.TIME.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 5=high", "display": { "unit": "1=low to 5=high" } }, "lp_lpi_trac_xq": { "title": "Logistics performance index: Ability to track and trace consignments (1=low to 5=high)", "description_from_producer": "Data are from the Logistics Performance Index survey conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. Respondents evaluate eight countries on six core dimensions on a scale from 1 (worst) to 5 (best). The eight countries are chosen based on the most important export and import markets of the respondent's country, random selection, and, for landlocked countries, neighboring countries that connect them with international markets. The 2023 LPI survey was conducted from September 6 to November 5, 2022. It provided 4,090 country assessments by 652 logistics professionals in 115 countries in all World Bank regions. Details of the survey methodology and index construction methodology are included in Appendix 5 of the 2023 LPI report available at: https://lpi.worldbank.org/report. Respondents evaluated the ability to track and trace consignments when shipping to the market, on a rating ranging from 1 (very low) to 5 (very high). Scores are averaged across all respondents.\n\n### Periodicity:\nEvery few years\n\n### Aggregation method:\nUnweighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator presents data from Logistics Performance Surveys conducted by the World Bank in partnership with academic and international institutions and private companies and individuals engaged in international logistics. The Logistics Performance Index (LPI) uses a structured online survey of logistics professionals at multinational freight forwarders and the main express carriers. The 2023 International LPI covers 139 countries. Each survey respondent rates eight overseas markets on six core components of logistics performance (the efficiency of customs and border management clearance, the quality of trade and transport infrastructure, the ease of arranging competitively priced shipments, the competence and quality of logistics services, the ability to track and trace consignments, and the frequency shipments reach consignees within scheduled or expected delivery times). The components are rated on a scale (lowest score to highest score) from 1 to 5. The eight countries are chosen based on the most important export and import markets of the country where the respondent is located, on random selection, and - for landlocked countries - on neighboring countries that form part of the land bridge connecting them with international markets. The method used to select the group of countries rated by each respondent varies by the characteristics of the country where the respondent is located. If respondents did not provide information for all six components, interpolation is used to fill in missing values. The missing values are replaced with the country mean response for each question, adjusted by the respondent's average deviation from the country mean in the answered questions.\nStatistical concept(s): The LPI is constructed from the six indicators using principal component analysis (PCA), a standard statistical technique used to reduce the dimensionality of a dataset. In the LPI, the inputs for PCA are country scores the questions covering the main six components, averaged across all respondents providing data on a given overseas market. Scores are normalized by subtracting the sample mean and dividing by the standard deviation before conducting PCA. The output from PCA is a single indicator - the LPI - that is a weighted average of those scores. The weights are chosen to maximize the percentage of variation in the LPI's original six indicators. To construct the international LPI, normalized scores for each of the six original indicators are multiplied by their component loadings and then summed. The component loadings represent the weight given to each original indicator in constructing the international LPI. Since the loadings are similar for all six, the international LPI is close to a simple average of the indicators. To account for the sampling error created by the LPI's survey-based dataset, LPI scores are presented with approximate 80 percent confidence intervals.\n\n### Development relevance:\nThe LPI measures on-the-ground trade logistics performance, helping national leaders, key policymakers, and private sector traders understand the challenges they and their trading partners face in reducing logistical barriers to international commerce. As the backbone of international trade, logistics encompasses freight transportation, warehousing, border clearance, payment systems, and many other functions. These functions are performed mostly by private service providers for private traders and owners of goods, but logistics is also important for the public policies of national governments and regional and international organizations. Because global supply chains are so varied and complex, the efficiency of logistics depends on government services, investments, and policies. Building infrastructure, developing a regulatory regime for transport services, and designing and implementing efficient customs clearance procedures are all areas where governments play an important role. The improvements in global logistics over the past two decades have been driven by innovation and a great increase in global trade. While policies and investments that enable good logistics practices help modernize the best-performing countries, logistics still lags in many developing countries. Indeed, the \"logistics gap\" evident in the first two editions of this report remains. The importance of logistics performance for economic growth, diversification, and poverty reduction has long been widely recognized. National governments can facilitate trade through investments in both \"hard\" and \"soft\" infrastructure. Countries have improved their logistics performance by implementing strategic and sustained interventions, mobilizing actors across traditional sector silos, and involving the private sector. Logistics is also increasingly important for sustainability. A focus on the environmental impacts of logistics practices is also included in the LPI.\n\n### Limitations and exceptions:\nThe Logistics Performance Index is an interactive benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics and what they can do to improve their performance. Despite being the most comprehensive data source for country logistics and trade facilitation, the LPI has two important limitations. First, the experience of international freight forwarders might not represent the broader logistics environment in poor countries, which often rely on traditional operators. And the international and traditional operators might differ in their interactions with government agencies - and in their service levels. Second, for landlocked countries and small-island states, the LPI might reflect access problems outside the country assessed, such as transit difficulties. The low rating of a landlocked country might not adequately reflect its trade facilitation efforts, which depend on the workings of complex international transit systems. Landlocked countries cannot eliminate transit inefficiencies with domestic reforms.\n\n### Other notes:\nThis indicator is based on the original Logistics Performance Index (LPI 1.0) methodology. An updated framework (LPI 2.0) will be released at https://lpi.worldbank.org/.", "origins": [ { "producer": "Logistics Performance Index (LPI) - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Connecting to Compete - Logistics Performance Index (LPI), World Bank (WB), uri: https://lpi.worldbank.org. Indicator LP.LPI.TRAC.XQ (https://data.worldbank.org/indicator/LP.LPI.TRAC.XQ). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/LP.LPI.TRAC.XQ", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1=low to 5=high", "display": { "unit": "1=low to 5=high" } }, "ms_mil_mprt_kd": { "title": "Arms imports (SIPRI trend indicator values)", "description_from_producer": "Arms transfers (imports) cover the volume of transfers of major arms through sales and gifts, and those made through manufacturing licenses. Data cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems, missiles, and ships. Figures are SIPRI Trend Indicator Values (TIVs). A '0' indicates that the volume of deliveries is between 0 and 0.5 million SIPRI TIV.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Stockholm International Peace Research Institute (SIPRI)'s Arms Transfers Program collects data on arms transfers from open sources. Since publicly available information is inadequate for tracking all weapons and other military equipment, SIPRI covers only what it terms major conventional weapons. Data cover the supply of weapons through sales, aid, gifts, and manufacturing licenses; therefore the term arms transfers rather than arms trade is used. SIPRI data also cover weapons supplied to or from rebel forces in an armed conflict as well as arms deliveries for which neither the supplier nor the recipient can be identified with acceptable certainty; these data are available in SIPRI's database.\n\nData cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems and other sensors, missiles, and ships designed for military use as well as some major components such as turrets for armored vehicles and engines. Excluded are other military equipment such as most small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services.\n\nWorld total includes arms transfers values for paramilitary groups.\n\nFor the method used for the SIPRI TIV see .\n\n### Development relevance:\nAlthough national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\n### Limitations and exceptions:\nSIPRI calculates the volume of transfers to, from and between all parties using the TIV and the number of weapon systems or subsystems delivered in a given year. This data is intended to provide a common unit to allow the measurement if trends in the flow of arms to particular countries and regions over time. Therefore, the main priority is to ensure that the TIV system remains consistent over time, and that any changes introduced are backdated.\n\nSIPRI TIV figures do not represent sales prices for arms transfers. They should therefore not be directly compared with gross domestic product (GDP), military expenditure, sales values or the financial value of export licences in an attempt to measure the economic burden of arms imports or the economic benefits of exports. They are best used as the raw data for calculating trends in international arms transfers over periods of time, global percentages for suppliers and recipients, and percentages for the volume of transfers to or from particular states.\n\nExcluded are transfers of other military equipment such as small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services.\n\n### Other notes:\nData for some countries are based on partial or uncertain data or rough estimates.", "origins": [ { "producer": "Stockholm International Peace Research Institute (SIPRI), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Arms Transfers Programme, Stockholm International Peace Research Institute (SIPRI), uri: https://armstransfers.sipri.org/ArmsTransfer/. Indicator MS.MIL.MPRT.KD (https://data.worldbank.org/indicator/MS.MIL.MPRT.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/MS.MIL.MPRT.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "SIPRI trend indicator values", "display": { "unit": "SIPRI trend indicator values" } }, "ms_mil_totl_p1": { "title": "Armed forces personnel, total", "description_from_producer": "Armed forces personnel are active duty military personnel, including paramilitary forces if the training, organization, equipment, and control suggest they may be used to support or replace regular military forces.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Military data on manpower represent quantitative assessment of the personnel strengths of the world's armed forces. The IISS collects the data from a wide variety of sources. The numbers are based on the most accurate data available to, or on the best estimate that can be made by the International Institute for Strategic Studies (IISS) at the time of its annual publication. The current WDI indicator includes active armed forces and active paramilitary (but not reservists). Armed forces personnel comprise all servicemen and women on full-time duty, including conscripts and long-term assignments from the Reserves (\u201cReserve\u201d describes formations and units not fully manned or operational in peacetime, but which can be mobilized by recalling reservists in an emergency). The indicator includes paramilitary forces. The source of the data (IISS) reports armed forces and paramilitary forces separately, however these figures are added for the purpose of computing this series. Home Guard units are counted as paramilitary.\n\n### Development relevance:\nAlthough national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\n### Limitations and exceptions:\nData excludes personnel not on active duty, therefore it underestimates the share of the labor force working for the defense establishment. The cooperation of governments of all countries listed in \u201cThe Military Balance\u201d has been sought by IISS and, in many cases, received. However, some data in \u201cThe Military Balance\u201d is estimated.\n\n### Other notes:\nData for some countries are based on partial or uncertain data or rough estimates.", "origins": [ { "producer": "The Military Balance - International Institute for Strategic Studies, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Military Balance, International Institute for Strategic Studies. Indicator MS.MIL.TOTL.P1 (https://data.worldbank.org/indicator/MS.MIL.TOTL.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/MS.MIL.TOTL.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ms_mil_totl_tf_zs": { "title": "Armed forces personnel (% of total labor force)", "description_from_producer": "Armed forces personnel are active duty military personnel, including paramilitary forces if the training, organization, equipment, and control suggest they may be used to support or replace regular military forces. Labor force comprises all people who meet the International Labour Organization's definition of the economically active population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Military data on manpower represent quantitative assessment of the personnel strengths of the world's armed forces. The numbers are based on the most accurate data available to, or, on the best estimate that can be made by the International Institute for Strategic Studies (IISS) at the time of its annual publication. The IISS collects the data from national governments.\n\nArmed forces personnel comprise all servicemen and women on full-time duty (including conscripts and long-term assignments from the Reserves). Reserve describes formations and units not fully manned or operational in peacetime, but which can be mobilized by recalling reservists in an emergency. IISS estimates of effective reservist strengths on the numbers available within five years of completing full-time service, unless there is good evidence that obligations are enforced for longer. Although paramilitary forces whose training, organization, equipment and control suggest they may be used to support or replace regular military forces, they are not included in the armed forces personnel. Home Guard units are counted as paramilitary.\n\nData are shown as percentage of total labor force. According to International Labour Organization (ILO), armed forces occupations include all jobs held by members of the armed forces. Members of the armed forces are those personnel who are currently serving in the armed forces, including auxiliary services, whether on a voluntary or compulsory basis, and who are not free to accept civilian employment and are subject to military discipline. Included are regular members of the army, navy, air force and other military services, as well as conscripts enrolled for military training or other service for a specified period. Excluded are persons in civilian employment of government establishments concerned with defense issues; police (other than military police); customs inspectors and members of border or other armed civilian services; persons who have been temporarily withdrawn from civilian life for a short period of military training or retraining, according to national requirements, and members of military reserves not currently on active service.\n\n### Development relevance:\nAlthough national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\n### Limitations and exceptions:\nData exclude personnel not on active duty, therefore they underestimate the share of the labor force working for the defense establishment. Governments rarely report the size of their armed forces, so such data typically come from intelligence sources. Unless otherwise indicated, reserves includes all reservists committed to rejoining the armed forces in an emergency, except when national reserve service obligations following conscription last almost a lifetime.\n\n### Other notes:\nData for some countries are based on partial or uncertain data or rough estimates.", "origins": [ { "producer": "The Military Balance - International Institute for Strategic Studies, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Military Balance, International Institute for Strategic Studies. Indicator MS.MIL.TOTL.TF.ZS (https://data.worldbank.org/indicator/MS.MIL.TOTL.TF.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/MS.MIL.TOTL.TF.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "numDecimalPlaces": 1, "shortUnit": "%", "unit": "%" } }, "ms_mil_xpnd_cd": { "title": "Military expenditure (current USD)", "description_from_producer": "Military spending USD, in current prices (converted at the exchange rate for the given year)\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI\u2019s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI\u2019s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates.\n\nMilitary spending as a share of GDP (\u201cmilitary burden\u201d) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit.\n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex\n\nRefer to Other notes for the Statistical Concept(s).\n\n### Development relevance:\nAlthough national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures as a share of gross domestic product (GDP) are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nAs an \"input\" measure military expenditures are not directly related to the \"output\" of military activities, capabilities, or security. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\n### Limitations and exceptions:\nSIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI\u2019s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries.\n\n### Other notes:\nStatistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country).\n\nSIPRI\u2019s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities\u2014like veterans\u2019 benefits or demobilization\u2014are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI\u2019s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition.\n\nData for some countries are based on partial or uncertain data or rough estimates. For additional details please refer to the military expenditure database on the SIPRI website: https://sipri.org/databases/milex", "origins": [ { "producer": "Stockholm International Peace Research Institute (SIPRI), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases. Indicator MS.MIL.XPND.CD (https://data.worldbank.org/indicator/MS.MIL.XPND.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/MS.MIL.XPND.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current USD", "display": { "unit": "current USD" } }, "ms_mil_xpnd_cn": { "title": "Military expenditure (current LCU)", "description_from_producer": "Military expenditure in local currency at current prices (by calendar year)\n\n### Statistical concept and methodology:\nMethodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI\u2019s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI\u2019s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates.\n\nMilitary spending as a share of GDP (\u201cmilitary burden\u201d) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit.\n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex\n\nRefer to Other notes for the Statistical Concept(s).\n\n### Development relevance:\nAlthough national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\n### Limitations and exceptions:\nSIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI\u2019s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries.\n\n### Other notes:\nStatistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country).\n\nSIPRI\u2019s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities\u2014like veterans\u2019 benefits or demobilization\u2014are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI\u2019s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition.", "origins": [ { "producer": "Stockholm International Peace Research Institute (SIPRI), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases. Indicator MS.MIL.XPND.CN (https://data.worldbank.org/indicator/MS.MIL.XPND.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/MS.MIL.XPND.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ms_mil_xpnd_gd_zs": { "title": "Military expenditure (% of GDP)", "description_from_producer": "Military expenditure by country as percentage of gross domestic product\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI\u2019s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI\u2019s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates.\n\nMilitary spending as a share of GDP (\u201cmilitary burden\u201d) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit.\n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex\nRefer to Other notes for the Statistical Concept(s).\n\n### Development relevance:\nAlthough national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures as a share of gross domestic product (GDP) are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nAs an \"input\" measure military expenditures are not directly related to the \"output\" of military activities, capabilities, or security. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\n### Limitations and exceptions:\nSIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI\u2019s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries.\n\n### Other notes:\nStatistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country).\n\nSIPRI\u2019s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities\u2014like veterans\u2019 benefits or demobilization\u2014are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI\u2019s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition.", "origins": [ { "producer": "Stockholm International Peace Research Institute (SIPRI), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases. Indicator MS.MIL.XPND.GD.ZS (https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ms_mil_xpnd_zs": { "title": "Military expenditure (% of general government expenditure)", "description_from_producer": "Military expenditure expressed as a percentage of general government expenditure\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Military expenditure data is collected from primary and secondary sources. Primary sources include official government publications such as national budgets, defense white papers, financial statistics, and responses to questionnaires from SIPRI, the UN, or the OSCE, as well as expert analyses of government budgets. Secondary sources draw on these primary materials and include international datasets produced by organizations like NATO and the IMF, as well as reference works such as the German Statistisches Jahrbuch, the Europa Yearbook, and Economist Intelligence Unit country reports. Other secondary sources are journals and newspapers. Historically, especially before 1988, secondary sources (notably IMF and UN statistics) were used more heavily due to limited availability of official national data. In recent years, the availability of primary government data has increased significantly.\n\nSIPRI uses government-reported military expenditure data as the baseline and only produces its own estimates when official data are incomplete or inconsistent across years. Estimates are created through detailed budget analysis or by merging overlapping data sources, giving priority to those that best fit SIPRI\u2019s definition, are up-to-date, and provide continuous time series. Older pre-1988 data often required combining secondary sources like IMF GFS and UNSY, which differ in definitions (e.g., excluding military pensions). SIPRI avoids making assumptions and does not estimate spending for countries lacking any official data. In SIPRI\u2019s database, estimated values appear in blue, while figures considered uncertain, because of weak sources or volatile conditions, appear in red. For recent years, budget projections and deflator-based adjustments are common but flagged only when uncertainty is unusually high.\n\nSIPRI presents military expenditure data on a calendar-year basis (except for the U.S., which uses financial years) and converts figures to constant prices using national consumer price indices to reflect opportunity costs. Local-currency data are converted to US dollars using average market exchange rates.\n\nMilitary spending as a share of GDP (\u201cmilitary burden\u201d) is calculated using nominal local-currency values for both military expenditure and GDP. SIPRI also provides military spending as a share of total government expenditure, where IMF data permit.\n\nFor additional information, please refer to the SIPRI website: https://www.sipri.org/databases/milex\n\nRefer to Other notes for the Statistical Concept(s).\n\n### Development relevance:\nAlthough national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures as a share of gross domestic product (GDP) are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nAs an \"input\" measure military expenditures are not directly related to the \"output\" of military activities, capabilities, or security. Comparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\n### Limitations and exceptions:\nSIPRI strives to compile reliable, consistent military expenditure data by assessing multiple sources, but accuracy depends on the quality and transparency of those sources. Challenges arise from two key issues: whether reported figures reflect actual spending and how closely they match SIPRI\u2019s definition. While data is generally accurate in developed and many developing countries, weak governance, corruption, and secret transfers in others can lead to major discrepancies. SIPRI sometimes makes estimates, when sources conflict or lack coverage, introducing uncertainty, especially for countries like China or the UAE. Definitions also vary: official defense budgets may omit pensions, paramilitary forces, or extra- and off-budget spending such as resource funds or military commercial activities, which can be substantial but often untraceable, particularly in Africa, the Middle East, and parts of Asia. SIPRI notes these gaps in footnotes, but where figures cannot be obtained, estimates remain incomplete, limiting comparability across countries.\n\n### Other notes:\nStatistical concept(s): Although the lack of sufficiently detailed data makes it difficult to apply a common definition of military expenditure on a worldwide basis, SIPRI has adopted a definition as a guideline. Where possible, SIPRI military expenditure data include all current and capital expenditure on: (a) the armed forces, including peacekeeping forces; (b) defense ministries and other government agencies engaged in defense projects; (c) paramilitary forces, when judged to be trained and equipped for military operations; and (d) military space activities.\n\nThis should include expenditure on: (i) personnel, including: salaries of military and civil personnel; b. retirement pensions of military personnel, and; social services for personnel; (ii) operations and maintenance; (iii) procurement; (iv) military research and development; (v) military infrastructure spending, including military bases; and (vi) military aid (in the military expenditure of the donor country).\n\nSIPRI\u2019s estimate of military aid includes financial contributions, training and operational costs, replacement costs of the military equipment stocks donated to recipients and payments to procure additional military equipment for the recipient. However, it does not include the estimated value of military equipment stocks donated.\n\nCivil defense and spending related to past military activities\u2014like veterans\u2019 benefits or demobilization\u2014are excluded. Because many countries do not publish data detailed enough to perfectly match SIPRI\u2019s definition, SIPRI often relies on national figures and prioritizes internal consistency over time rather than strict cross-country uniformity. As a result, SIPRI data are most reliable for analyzing trends rather than precise comparisons between countries, and users should consult footnotes for known deviations from the definition.\n\nData for some countries are based on partial or uncertain data or rough estimates.", "origins": [ { "producer": "Stockholm International Peace Research Institute (SIPRI), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "SIPRI Military Expenditure Database, Stockholm International Peace Research Institute (SIPRI), uri: https://www.sipri.org/databases. Indicator MS.MIL.XPND.ZS (https://data.worldbank.org/indicator/MS.MIL.XPND.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/MS.MIL.XPND.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of central government expenditure", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of central government expenditure" } }, "ms_mil_xprt_kd": { "title": "Arms exports (SIPRI trend indicator values)", "description_from_producer": "Arms transfers (exports) cover the volume of transfers of major arms through sales and gifts, and those made through manufacturing licenses. Data cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems, missiles, and ships. Figures are SIPRI Trend Indicator Values (TIVs). A '0' indicates that the volume of deliveries is between 0 and 0.5 million SIPRI TIV.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Stockholm International Peace Research Institute (SIPRI)'s Arms Transfers Program collects data on arms transfers from open sources. Since publicly available information is inadequate for tracking all weapons and other military equipment, SIPRI covers only what it terms major conventional weapons. Data cover the supply of weapons through sales, aid, gifts, and manufacturing licenses; therefore the term arms transfers rather than arms trade is used. SIPRI data also cover weapons supplied to or from rebel forces in an armed conflict as well as arms deliveries for which neither the supplier nor the recipient can be identified with acceptable certainty; these data are available in SIPRI's database.\n\nData cover major conventional weapons such as aircraft, armored vehicles, artillery, radar systems and other sensors, missiles, and ships designed for military use as well as some major components such as turrets for armored vehicles and engines. Excluded are other military equipment such as most small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services.\n\nWorld total includes arms transfers values for paramilitary groups.\n\nFor the method used for the SIPRI TIV see .\n\n### Development relevance:\nAlthough national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\n### Limitations and exceptions:\nSIPRI calculates the volume of transfers to, from and between all parties using the TIV and the number of weapon systems or subsystems delivered in a given year. This data is intended to provide a common unit to allow the measurement if trends in the flow of arms to particular countries and regions over time. Therefore, the main priority is to ensure that the TIV system remains consistent over time, and that any changes introduced are backdated.\n\nSIPRI TIV figures do not represent sales prices for arms transfers. They should therefore not be directly compared with gross domestic product (GDP), military expenditure, sales values or the financial value of export licences in an attempt to measure the economic burden of arms imports or the economic benefits of exports. They are best used as the raw data for calculating trends in international arms transfers over periods of time, global percentages for suppliers and recipients, and percentages for the volume of transfers to or from particular states.\n\nExcluded are transfers of other military equipment such as small arms and light weapons, trucks, small artillery, ammunition, support equipment, technology transfers, and other services.\n\n### Other notes:\nData for some countries are based on partial or uncertain data or rough estimates.", "origins": [ { "producer": "Stockholm International Peace Research Institute (SIPRI), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Arms Transfers Programme, Stockholm International Peace Research Institute (SIPRI), uri: https://armstransfers.sipri.org/ArmsTransfer/. Indicator MS.MIL.XPRT.KD (https://data.worldbank.org/indicator/MS.MIL.XPRT.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/MS.MIL.XPRT.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "SIPRI trend indicator values", "display": { "unit": "SIPRI trend indicator values" } }, "ne_con_govt_cd": { "title": "General government final consumption expenditure (current US$)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.GOVT.CD (https://data.worldbank.org/indicator/NE.CON.GOVT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.GOVT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ne_con_govt_cn": { "title": "General government final consumption expenditure (current LCU)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.GOVT.CN (https://data.worldbank.org/indicator/NE.CON.GOVT.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.GOVT.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ne_con_govt_kd": { "title": "General government final consumption expenditure (constant 2015 US$)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.)\n\n\n\n\n\n\n\n\n\nTo obtain government consumption in constant prices, countries may deflate current values by applying a wage (price) index or extrapolate from the change in government employment. Neither technique captures improvements in productivity or changes in the quality of government services.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.GOVT.KD (https://data.worldbank.org/indicator/NE.CON.GOVT.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.GOVT.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "ne_con_govt_kd_zg": { "title": "General government final consumption expenditure (annual % growth)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.GOVT.KD.ZG (https://data.worldbank.org/indicator/NE.CON.GOVT.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.GOVT.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "ne_con_govt_kn": { "title": "General government final consumption expenditure (constant LCU)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.GOVT.KN (https://data.worldbank.org/indicator/NE.CON.GOVT.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.GOVT.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ne_con_govt_zs": { "title": "General government final consumption expenditure (% of GDP)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. General government FCE includes all government current expenditures for purchases of goods and services (including compensation of employees), and most expenditures on national defense and security, but excludes government military expenditures that are part of government capital formation. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.CON.GOVT.ZS (https://data.worldbank.org/indicator/NE.CON.GOVT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.GOVT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ne_con_pcap_pp_cd": { "title": "Households and NPISHs Final consumption expenditure per capita, PPP (current international $)", "description_from_producer": "This indicator provides values for final consumption expenditure per person expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\nHouseholds and NPISHs final consumption expenditure includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world\u2019s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations\u2019 databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), note: This information is for PPP conversion factors., publisher: International Comparison Program (ICP), date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, note: This information is for PPP conversion factors., publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB), note: This information is for households and NPISHs Final consumption expenditure per capita.;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD), note: This information is for households and NPISHs Final consumption expenditure per capita.;\nWorld Economic Outlook database, International Monetary Fund (IMF), note: This information is for households and NPISHs Final consumption expenditure per capita. Indicator NE.CON.PCAP.PP.CD (https://data.worldbank.org/indicator/NE.CON.PCAP.PP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PCAP.PP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ne_con_pcap_pp_kd": { "title": "Households and NPISHs Final consumption expenditure per capita, PPP (constant 2021 international $)", "description_from_producer": "This indicator provides values for final consumption expenditure per person expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\nHouseholds and NPISHs final consumption expenditure includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world\u2019s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations\u2019 databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), note: This information is for PPP conversion factors., publisher: International Comparison Program (ICP), date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, note: This information is for PPP conversion factors., publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, note: This information is for PPP conversion factors., publisher: OECD;\nStaff estimates, World Bank (WB), note: This information is for households and NPISHs Final consumption expenditure per capita.;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD), note: This information is for households and NPISHs Final consumption expenditure per capita.;\nWorld Economic Outlook database, International Monetary Fund (IMF), note: This information is for households and NPISHs Final consumption expenditure per capita. Indicator NE.CON.PCAP.PP.KD (https://data.worldbank.org/indicator/NE.CON.PCAP.PP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PCAP.PP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ne_con_prvt_cd": { "title": "Households and NPISHs Final consumption expenditure (current US$)", "description_from_producer": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.CON.PRVT.CD (https://data.worldbank.org/indicator/NE.CON.PRVT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PRVT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ne_con_prvt_cn": { "title": "Households and NPISHs Final consumption expenditure (current LCU)", "description_from_producer": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.CON.PRVT.CN (https://data.worldbank.org/indicator/NE.CON.PRVT.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PRVT.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ne_con_prvt_cn_ad": { "title": "Households and NPISHs final consumption expenditure: linked series (current LCU)", "description_from_producer": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "World Bank, OECD, and IMF", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NE.CON.PRVT.CN.AD (https://data.worldbank.org/indicator/NE.CON.PRVT.CN.AD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PRVT.CN.AD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ne_con_prvt_kd": { "title": "Households and NPISHs Final consumption expenditure (constant 2015 US$)", "description_from_producer": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total. Household final consumption expenditure is often estimated as a residual, by subtracting all other known expenditures from GDP. The resulting aggregate may incorporate fairly large discrepancies. When household consumption is calculated separately, many of the estimates are based on household surveys, which tend to be one-year studies with limited coverage. Thus the estimates quickly become outdated and must be supplemented by estimates using price- and quantity-based statistical procedures. Complicating the issue, in many developing countries the distinction between cash outlays for personal business and those for household use may be blurred.\n\n\n\n\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.)", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.PRVT.KD (https://data.worldbank.org/indicator/NE.CON.PRVT.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PRVT.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ne_con_prvt_kd_zg": { "title": "Households and NPISHs Final consumption expenditure (annual % growth)", "description_from_producer": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.PRVT.KD.ZG (https://data.worldbank.org/indicator/NE.CON.PRVT.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PRVT.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ne_con_prvt_kn": { "title": "Households and NPISHs Final consumption expenditure (constant LCU)", "description_from_producer": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.PRVT.KN (https://data.worldbank.org/indicator/NE.CON.PRVT.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PRVT.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ne_con_prvt_pc_kd": { "title": "Households and NPISHs Final consumption expenditure per capita (constant 2015 US$)", "description_from_producer": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total. Household final consumption expenditure is often estimated as a residual, by subtracting all other known expenditures from GDP. The resulting aggregate may incorporate fairly large discrepancies. When household consumption is calculated separately, many of the estimates are based on household surveys, which tend to be one-year studies with limited coverage. Thus the estimates quickly become outdated and must be supplemented by estimates using price- and quantity-based statistical procedures. Complicating the issue, in many developing countries the distinction between cash outlays for personal business and those for household use may be blurred.\n\n\n\n\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.)", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.CON.PRVT.PC.KD (https://data.worldbank.org/indicator/NE.CON.PRVT.PC.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PRVT.PC.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ne_con_prvt_pc_kd_zg": { "title": "Households and NPISHs Final consumption expenditure per capita growth (annual %)", "description_from_producer": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.CON.PRVT.PC.KD.ZG (https://data.worldbank.org/indicator/NE.CON.PRVT.PC.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PRVT.PC.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" } }, "ne_con_prvt_pp_cd": { "title": "Households and NPISHs Final consumption expenditure, PPP (current international $)", "description_from_producer": "This indicator provides values for final consumption expenditure expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\n\nHouseholds and NPISHs final consumption expenditure includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world's countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations' databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP\u2019s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NE.CON.PRVT.PP.CD (https://data.worldbank.org/indicator/NE.CON.PRVT.PP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PRVT.PP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current international $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current international $" } }, "ne_con_prvt_pp_kd": { "title": "Households and NPISHs Final consumption expenditure, PPP (constant 2021 international $)", "description_from_producer": "This indicator provides values for final consumption expenditure expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\n\nHouseholds and NPISHs final consumption expenditure includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world\u2019s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations\u2019 databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP\u2019s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NE.CON.PRVT.PP.KD (https://data.worldbank.org/indicator/NE.CON.PRVT.PP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PRVT.PP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ne_con_prvt_zs": { "title": "Households and NPISHs final consumption expenditure (% of GDP)", "description_from_producer": "This field includes expenditure on goods and services by the Household and NPISH sector for the direct satisfaction of human needs or wants, whether individual or collective. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total. Household final consumption expenditure is often estimated as a residual, by subtracting all other known expenditures from GDP. The resulting aggregate may incorporate fairly large discrepancies. When household consumption is calculated separately, many of the estimates are based on household surveys, which tend to be one-year studies with limited coverage. Thus the estimates quickly become outdated and must be supplemented by estimates using price- and quantity-based statistical procedures. Complicating the issue, in many developing countries the distinction between cash outlays for personal business and those for household use may be blurred.\n\n\n\n\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.PRVT.ZS (https://data.worldbank.org/indicator/NE.CON.PRVT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.PRVT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ne_con_totl_cd": { "title": "Final consumption expenditure (current US$)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.CON.TOTL.CD (https://data.worldbank.org/indicator/NE.CON.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ne_con_totl_cn": { "title": "Final consumption expenditure (current LCU)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.TOTL.CN (https://data.worldbank.org/indicator/NE.CON.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ne_con_totl_kd": { "title": "Final consumption expenditure (constant 2015 US$)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.TOTL.KD (https://data.worldbank.org/indicator/NE.CON.TOTL.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.TOTL.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "ne_con_totl_kd_zg": { "title": "Final consumption expenditure (annual % growth)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.TOTL.KD.ZG (https://data.worldbank.org/indicator/NE.CON.TOTL.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.TOTL.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "ne_con_totl_kn": { "title": "Final consumption expenditure (constant LCU)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.TOTL.KN (https://data.worldbank.org/indicator/NE.CON.TOTL.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.TOTL.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ne_con_totl_zs": { "title": "Final consumption expenditure (% of GDP)", "description_from_producer": "Final consumption expenditure is expenditure on goods and services by resident institutional units for the direct satisfaction of human needs or wants, whether individual or collective. Final consumption expenditure can be measured for households, general government, the central bank and non-profit institutions serving households. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.CON.TOTL.ZS (https://data.worldbank.org/indicator/NE.CON.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.CON.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ne_dab_defl_zs": { "title": "Gross national expenditure deflator (base year varies by country)", "description_from_producer": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. A deflator is the ratio of an indicator in current prices over the same series in constant prices. The base year varies by country.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.DAB.DEFL.ZS (https://data.worldbank.org/indicator/NE.DAB.DEFL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.DAB.DEFL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "base year varies by country", "display": { "unit": "base year varies by country" } }, "ne_dab_totl_cd": { "title": "Gross national expenditure (current US$)", "description_from_producer": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.DAB.TOTL.CD (https://data.worldbank.org/indicator/NE.DAB.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.DAB.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ne_dab_totl_cn": { "title": "Gross national expenditure (current LCU)", "description_from_producer": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.DAB.TOTL.CN (https://data.worldbank.org/indicator/NE.DAB.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.DAB.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ne_dab_totl_kd": { "title": "Gross national expenditure (constant 2015 US$)", "description_from_producer": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.DAB.TOTL.KD (https://data.worldbank.org/indicator/NE.DAB.TOTL.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.DAB.TOTL.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "ne_dab_totl_kn": { "title": "Gross national expenditure (constant LCU)", "description_from_producer": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.DAB.TOTL.KN (https://data.worldbank.org/indicator/NE.DAB.TOTL.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.DAB.TOTL.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ne_dab_totl_zs": { "title": "Gross national expenditure (% of GDP)", "description_from_producer": "Gross national expenditure is the sum of household final consumption expenditure, general government final consumption expenditure, and gross capital formation. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.DAB.TOTL.ZS (https://data.worldbank.org/indicator/NE.DAB.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.DAB.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ne_exp_gnfs_cd": { "title": "Exports of goods and services (current US$)", "description_from_producer": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicators of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or correspond to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.EXP.GNFS.CD (https://data.worldbank.org/indicator/NE.EXP.GNFS.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.EXP.GNFS.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ne_exp_gnfs_cn": { "title": "Exports of goods and services (current LCU)", "description_from_producer": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.EXP.GNFS.CN (https://data.worldbank.org/indicator/NE.EXP.GNFS.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.EXP.GNFS.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ne_exp_gnfs_kd": { "title": "Exports of goods and services (constant 2015 US$)", "description_from_producer": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.EXP.GNFS.KD (https://data.worldbank.org/indicator/NE.EXP.GNFS.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.EXP.GNFS.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "ne_exp_gnfs_kd_zg": { "title": "Exports of goods and services (annual % growth)", "description_from_producer": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.EXP.GNFS.KD.ZG (https://data.worldbank.org/indicator/NE.EXP.GNFS.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.EXP.GNFS.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "ne_exp_gnfs_kn": { "title": "Exports of goods and services (constant LCU)", "description_from_producer": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.EXP.GNFS.KN (https://data.worldbank.org/indicator/NE.EXP.GNFS.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.EXP.GNFS.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ne_exp_gnfs_zs": { "title": "Exports of goods and services (% of GDP)", "description_from_producer": "Exports of goods includes changes in the economic ownership of goods from residents of the compiling economy to non-residents, irrespective of physical movement of goods across national borders. Exports of services includes services provided by residents to non-residents. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicators of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or correspond to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.EXP.GNFS.ZS (https://data.worldbank.org/indicator/NE.EXP.GNFS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.EXP.GNFS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ne_gdi_fprv_cn": { "title": "Gross fixed capital formation, private sector (current LCU)", "description_from_producer": "Private investment covers outlays by the private sector (including private nonprofit agencies) on additions to its fixed domestic assets. Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.FPRV.CN (https://data.worldbank.org/indicator/NE.GDI.FPRV.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.FPRV.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ne_gdi_fprv_zs": { "title": "Gross fixed capital formation, private sector (% of GDP)", "description_from_producer": "Private investment covers outlays by the private sector (including private nonprofit agencies) on additions to its fixed domestic assets. Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.FPRV.ZS (https://data.worldbank.org/indicator/NE.GDI.FPRV.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.FPRV.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ne_gdi_ftot_cd": { "title": "Gross fixed capital formation (current US$)", "description_from_producer": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.GDI.FTOT.CD (https://data.worldbank.org/indicator/NE.GDI.FTOT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.FTOT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ne_gdi_ftot_cn": { "title": "Gross fixed capital formation (current LCU)", "description_from_producer": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.FTOT.CN (https://data.worldbank.org/indicator/NE.GDI.FTOT.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.FTOT.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ne_gdi_ftot_kd": { "title": "Gross fixed capital formation (constant 2015 US$)", "description_from_producer": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.FTOT.KD (https://data.worldbank.org/indicator/NE.GDI.FTOT.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.FTOT.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "ne_gdi_ftot_kd_zg": { "title": "Gross fixed capital formation (annual % growth)", "description_from_producer": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.GDI.FTOT.KD.ZG (https://data.worldbank.org/indicator/NE.GDI.FTOT.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.FTOT.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "ne_gdi_ftot_kn": { "title": "Gross fixed capital formation (constant LCU)", "description_from_producer": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.FTOT.KN (https://data.worldbank.org/indicator/NE.GDI.FTOT.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.FTOT.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ne_gdi_ftot_zs": { "title": "Gross fixed capital formation (% of GDP)", "description_from_producer": "Gross fixed capital formation includes acquisitions less disposals of fixed assets during the accounting period, including certain specified expenditures on services that add to the value of non-produced assets. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.FTOT.ZS (https://data.worldbank.org/indicator/NE.GDI.FTOT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.FTOT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ne_gdi_stkb_cd": { "title": "Changes in inventories (current US$)", "description_from_producer": "Changes in inventories is the value of entries into inventories less the value of withdrawals and less the value of any recurrent losses of goods held in inventories during the accounting period.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.STKB.CD (https://data.worldbank.org/indicator/NE.GDI.STKB.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.STKB.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ne_gdi_stkb_cn": { "title": "Changes in inventories (current LCU)", "description_from_producer": "Changes in inventories is the value of entries into inventories less the value of withdrawals and less the value of any recurrent losses of goods held in inventories during the accounting period.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicators of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.GDI.STKB.CN (https://data.worldbank.org/indicator/NE.GDI.STKB.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.STKB.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ne_gdi_stkb_kn": { "title": "Changes in inventories (constant LCU)", "description_from_producer": "Changes in inventories is the value of entries into inventories less the value of withdrawals and less the value of any recurrent losses of goods held in inventories during the accounting period.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.STKB.KN (https://data.worldbank.org/indicator/NE.GDI.STKB.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.STKB.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ne_gdi_totl_cd": { "title": "Gross capital formation (current US$)", "description_from_producer": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\n\n\n\n\nData on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow method using data from production, trade, and construction activities. The quality of data on government fixed capital formation depends on the quality of government accounting systems (which tend to be weak in developing countries). Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable.\n\n\n\n\n\n\n\n\n\nEstimates of changes in inventories are rarely complete but usually include the most important activities or commodities. In some countries these estimates are derived as a composite residual along with household final consumption expenditure. According to national accounts conventions, adjustments should be made for appreciation of the value of inventory holdings due to price changes, but this is not always done. In highly inflationary economies this element can be substantial.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.TOTL.CD (https://data.worldbank.org/indicator/NE.GDI.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ne_gdi_totl_cn": { "title": "Gross capital formation (current LCU)", "description_from_producer": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.TOTL.CN (https://data.worldbank.org/indicator/NE.GDI.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ne_gdi_totl_kd": { "title": "Gross capital formation (constant 2015 US$)", "description_from_producer": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\n\n\n\n\nData on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow method using data from production, trade, and construction activities. The quality of data on government fixed capital formation depends on the quality of government accounting systems (which tend to be weak in developing countries). Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable.\n\n\n\n\n\n\n\n\n\nEstimates of changes in inventories are rarely complete but usually include the most important activities or commodities. In some countries these estimates are derived as a composite residual along with household final consumption expenditure. According to national accounts conventions, adjustments should be made for appreciation of the value of inventory holdings due to price changes, but this is not always done. In highly inflationary economies this element can be substantial.\n\n\n\n\n\n\n\n\n\nMeasures of growth in consumption and capital formation are subject to two kinds of inaccuracy. The first stems from the difficulty of measuring expenditures at current price levels. The second arises in deflating current price data to measure volume growth, where results depend on the relevance and reliability of the price indexes and weights used. Measuring price changes is more difficult for investment goods than for consumption goods because of the one-time nature of many investments and because the rate of technological progress in capital goods makes capturing change in quality difficult. (An example is computers - prices have fallen as quality has improved.) Several countries estimate capital formation from the supply side, identifying capital goods entering an economy directly from detailed production and international trade statistics. This means that the price indexes used in deflating production and international trade, reflecting delivered or offered prices, will determine the deflator for capital formation expenditures on the demand side.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.TOTL.KD (https://data.worldbank.org/indicator/NE.GDI.TOTL.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.TOTL.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "ne_gdi_totl_kd_zg": { "title": "Gross capital formation (annual % growth)", "description_from_producer": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.TOTL.KD.ZG (https://data.worldbank.org/indicator/NE.GDI.TOTL.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.TOTL.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "ne_gdi_totl_kn": { "title": "Gross capital formation (constant LCU)", "description_from_producer": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.TOTL.KN (https://data.worldbank.org/indicator/NE.GDI.TOTL.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.TOTL.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ne_gdi_totl_zs": { "title": "Gross capital formation (% of GDP)", "description_from_producer": "Gross capital formation includes acquisitions less disposals of produced assets for purposes of fixed capital formation, inventories or valuables. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\n\n\n\n\nData on capital formation may be estimated from direct surveys of enterprises and administrative records or based on the commodity flow method using data from production, trade, and construction activities. The quality of data on government fixed capital formation depends on the quality of government accounting systems (which tend to be weak in developing countries). Measures of fixed capital formation by households and corporations - particularly capital outlays by small, unincorporated enterprises - are usually unreliable.\n\n\n\n\n\n\n\n\n\nEstimates of changes in inventories are rarely complete but usually include the most important activities or commodities. In some countries these estimates are derived as a composite residual along with household final consumption expenditure. According to national accounts conventions, adjustments should be made for appreciation of the value of inventory holdings due to price changes, but this is not always done. In highly inflationary economies this element can be substantial.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.GDI.TOTL.ZS (https://data.worldbank.org/indicator/NE.GDI.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.GDI.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ne_imp_gnfs_cd": { "title": "Imports of goods and services (current US$)", "description_from_producer": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.IMP.GNFS.CD (https://data.worldbank.org/indicator/NE.IMP.GNFS.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.IMP.GNFS.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ne_imp_gnfs_cn": { "title": "Imports of goods and services (current LCU)", "description_from_producer": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.IMP.GNFS.CN (https://data.worldbank.org/indicator/NE.IMP.GNFS.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.IMP.GNFS.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ne_imp_gnfs_kd": { "title": "Imports of goods and services (constant 2015 US$)", "description_from_producer": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.IMP.GNFS.KD (https://data.worldbank.org/indicator/NE.IMP.GNFS.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.IMP.GNFS.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "ne_imp_gnfs_kd_zg": { "title": "Imports of goods and services (annual % growth)", "description_from_producer": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.IMP.GNFS.KD.ZG (https://data.worldbank.org/indicator/NE.IMP.GNFS.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.IMP.GNFS.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "ne_imp_gnfs_kn": { "title": "Imports of goods and services (constant LCU)", "description_from_producer": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.IMP.GNFS.KN (https://data.worldbank.org/indicator/NE.IMP.GNFS.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.IMP.GNFS.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ne_imp_gnfs_zs": { "title": "Imports of goods and services (% of GDP)", "description_from_producer": "Imports of goods includes change in the economic ownership of goods from non-residents to\n\n\n\n\nresidents of the compiling economy, irrespective of physical movement of goods across national borders. Imports of services includes services provided by non-residents to residents. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.\n\n### Limitations and exceptions:\nBecause policymakers have tended to focus on fostering the growth of output, and because data on production are easier to collect than data on spending, many countries generate their primary estimate of GDP using the production approach. Moreover, many countries do not estimate all the components of national expenditures but instead derive some of the main aggregates indirectly using GDP (based on the production approach) as the control total.\n\n\n\n\n\n\n\n\n\nData on exports and imports are compiled from customs reports and balance of payments data. Although the data from the payments side provide reasonably reliable records of cross-border transactions, they may not adhere strictly to the appropriate definitions of valuation and timing used in the balance of payments or corresponds to the change-of ownership criterion. This issue has assumed greater significance with the increasing globalization of international business. Neither customs nor balance of payments data usually capture the illegal transactions that occur in many countries. Goods carried by travelers across borders in legal but unreported shuttle trade may further distort trade statistics.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NE.IMP.GNFS.ZS (https://data.worldbank.org/indicator/NE.IMP.GNFS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.IMP.GNFS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ne_rsb_gnfs_cd": { "title": "External balance on goods and services (current US$)", "description_from_producer": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.RSB.GNFS.CD (https://data.worldbank.org/indicator/NE.RSB.GNFS.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.RSB.GNFS.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ne_rsb_gnfs_cn": { "title": "External balance on goods and services (current LCU)", "description_from_producer": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.RSB.GNFS.CN (https://data.worldbank.org/indicator/NE.RSB.GNFS.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.RSB.GNFS.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ne_rsb_gnfs_kn": { "title": "External balance on goods and services (constant LCU)", "description_from_producer": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.RSB.GNFS.KN (https://data.worldbank.org/indicator/NE.RSB.GNFS.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.RSB.GNFS.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ne_rsb_gnfs_zs": { "title": "External balance on goods and services (% of GDP)", "description_from_producer": "The balance of international trade in goods and services is the difference between the exports and imports of goods and services. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.RSB.GNFS.ZS (https://data.worldbank.org/indicator/NE.RSB.GNFS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.RSB.GNFS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ne_trd_gnfs_zs": { "title": "Trade (% of GDP)", "description_short": "Trade in goods and services as a share of GDP, shown as a percentage.", "description_from_producer": "Trade is the sum of exports and imports of goods and services. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the expenditure approach used to calculate GDP, which focuses on the total amount of spending on final goods and services within an economy over a specific period. Unlike the production approach, which looks at the supply side by summing the value of output produced by all sectors, the expenditure approach looks at the demand side by summing all expenditures. This demand-side analysis provides insights into the spending behaviors of different sectors, including households, businesses, the government, and foreign entities. Also, by breaking down expenditures into categories like consumption, investment, government spending, and net exports, it helps identify which components are driving or hindering economic growth. This approach can thus be used to assess the effectiveness of fiscal and monetary policies. Overall, the expenditure approach is crucial for understanding the dynamics of an economy, guiding policy decisions, and providing a comprehensive view of economic activity from the perspective of total spending.", "description_key": "- This indicator measures a country\u2019s total trade (exports and imports) relative to the size of its economy.\n- Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. Imports represent the value of all goods and other market services received from the rest of the world. In both cases, they include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NE.TRD.GNFS.ZS (https://data.worldbank.org/indicator/NE.TRD.GNFS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NE.TRD.GNFS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" }, "presentation": { "topic_tags": [ "Trade & Globalization" ] } }, "nv_agr_empl_kd": { "title": "Agriculture, forestry, and fishing, value added per worker (constant 2015 US$)", "description_from_producer": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Value added per worker is calculated by dividing value added of a sector by the number employed in the sector.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nFor comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD].", "origins": [ { "producer": "World Bank (World Development Indicators) and ILO (ILOSTAT)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Development Indicators database, World Bank (WB);\nILOSTAT database, International Labour Organization (ILO). Indicator NV.AGR.EMPL.KD (https://data.worldbank.org/indicator/NV.AGR.EMPL.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.AGR.EMPL.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "nv_agr_totl_cd": { "title": "Agriculture, forestry, and fishing, value added (current US$)", "description_from_producer": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicators of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives a detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nAmong the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.AGR.TOTL.CD (https://data.worldbank.org/indicator/NV.AGR.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.AGR.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "nv_agr_totl_cn": { "title": "Agriculture, forestry, and fishing, value added (current LCU)", "description_from_producer": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NV.AGR.TOTL.CN (https://data.worldbank.org/indicator/NV.AGR.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.AGR.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "nv_agr_totl_kd": { "title": "Agriculture, forestry, and fishing, value added (constant 2015 US$)", "description_from_producer": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nAmong the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NV.AGR.TOTL.KD (https://data.worldbank.org/indicator/NV.AGR.TOTL.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.AGR.TOTL.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "nv_agr_totl_kd_zg": { "title": "Agriculture, forestry, and fishing, value added (annual % growth)", "description_from_producer": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nAmong the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NV.AGR.TOTL.KD.ZG (https://data.worldbank.org/indicator/NV.AGR.TOTL.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.AGR.TOTL.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "nv_agr_totl_kn": { "title": "Agriculture, forestry, and fishing, value added (constant LCU)", "description_from_producer": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NV.AGR.TOTL.KN (https://data.worldbank.org/indicator/NV.AGR.TOTL.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.AGR.TOTL.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "nv_agr_totl_zs": { "title": "Agriculture, forestry, and fishing, value added (% of GDP)", "description_from_producer": "Agriculture, forestry, and fishing corresponds to ISIC (Rev. 4) divisions 01-03 and includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period. Note: For VAB countries, gross value added at factor cost is used as the denominator.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nAmong the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money. Agricultural production often must be estimated indirectly, using a combination of methods involving estimates of inputs, yields, and area under cultivation. This approach sometimes leads to crude approximations that can differ from the true values over time and across crops for reasons other than climate conditions or farming techniques. Similarly, agricultural inputs that cannot easily be allocated to specific outputs are frequently \"netted out\" using equally crude and ad hoc approximations.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NV.AGR.TOTL.ZS (https://data.worldbank.org/indicator/NV.AGR.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.AGR.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "nv_fsm_totl_cn": { "title": "Financial intermediary services indirectly Measured (FISIM) (current LCU)", "description_from_producer": "Financial intermediation services which are implicitly charged in the form of either the difference between a reference rate and the interest rate actually paid to depositors, or the difference between the interest rate charged to borrowers and a reference rate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NV.FSM.TOTL.CN (https://data.worldbank.org/indicator/NV.FSM.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.FSM.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "nv_fsm_totl_kn": { "title": "Financial intermediary services indirectly Measured (FISIM) (constant LCU)", "description_from_producer": "Financial intermediation services which are implicitly charged in the form of either the difference between a reference rate and the interest rate actually paid to depositors, or the difference between the interest rate charged to borrowers and a reference rate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NV.FSM.TOTL.KN (https://data.worldbank.org/indicator/NV.FSM.TOTL.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.FSM.TOTL.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "nv_ind_empl_kd": { "title": "Industry (including construction), value added per worker (constant 2015 US$)", "description_from_producer": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. The core indicator has been divided by the number of workers in this sector to derive a measure of labor productivity. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Value added per worker is calculated by dividing value added of a sector by the number employed in the sector.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nFor comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD].", "origins": [ { "producer": "World Bank (World Development Indicators) and ILO (ILOSTAT)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Development Indicators database, World Bank (WB);\nILOSTAT database, International Labour Organization (ILO). Indicator NV.IND.EMPL.KD (https://data.worldbank.org/indicator/NV.IND.EMPL.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.EMPL.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "nv_ind_manf_cd": { "title": "Manufacturing, value added (current US$)", "description_from_producer": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical transformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIn establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.MANF.CD (https://data.worldbank.org/indicator/NV.IND.MANF.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.MANF.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "nv_ind_manf_cn": { "title": "Manufacturing, value added (current LCU)", "description_from_producer": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical transformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.MANF.CN (https://data.worldbank.org/indicator/NV.IND.MANF.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.MANF.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "nv_ind_manf_kd": { "title": "Manufacturing, value added (constant 2015 US$)", "description_from_producer": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical transformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIdeally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.MANF.KD (https://data.worldbank.org/indicator/NV.IND.MANF.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.MANF.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "nv_ind_manf_kd_zg": { "title": "Manufacturing, value added (annual % growth)", "description_from_producer": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical transformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIdeally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.MANF.KD.ZG (https://data.worldbank.org/indicator/NV.IND.MANF.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.MANF.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "nv_ind_manf_kn": { "title": "Manufacturing, value added (constant LCU)", "description_from_producer": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical transformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.MANF.KN (https://data.worldbank.org/indicator/NV.IND.MANF.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.MANF.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "nv_ind_manf_zs": { "title": "Manufacturing, value added (% of GDP)", "description_from_producer": "Manufacturing includes industries classified in ISIC (Rev. 3) major division C and is defined as the physical or chemical transformation of materials or components into new products. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicators of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIdeally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.MANF.ZS (https://data.worldbank.org/indicator/NV.IND.MANF.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.MANF.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "nv_ind_totl_cd": { "title": "Industry (including construction), value added (current US$)", "description_from_producer": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIdeally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.TOTL.CD (https://data.worldbank.org/indicator/NV.IND.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "nv_ind_totl_cn": { "title": "Industry (including construction), value added (current LCU)", "description_from_producer": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.TOTL.CN (https://data.worldbank.org/indicator/NV.IND.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "nv_ind_totl_kd": { "title": "Industry (including construction), value added (constant 2015 US$)", "description_from_producer": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIdeally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.TOTL.KD (https://data.worldbank.org/indicator/NV.IND.TOTL.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.TOTL.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "nv_ind_totl_kd_zg": { "title": "Industry (including construction), value added (annual % growth)", "description_from_producer": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIdeally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.TOTL.KD.ZG (https://data.worldbank.org/indicator/NV.IND.TOTL.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.TOTL.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "nv_ind_totl_kn": { "title": "Industry (including construction), value added (constant LCU)", "description_from_producer": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.TOTL.KN (https://data.worldbank.org/indicator/NV.IND.TOTL.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.TOTL.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "nv_ind_totl_zs": { "title": "Industry (including construction), value added (% of GDP)", "description_from_producer": "Industry (including construction) corresponds to ISIC (Rev.4) divisions 05-43. It is comprised of mining, manufacturing, construction, electricity, water, and gas industries. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIdeally, industrial output should be measured through regular censuses and surveys of firms. But in most developing countries such surveys are infrequent, so earlier survey results must be extrapolated using an appropriate indicator. The choice of sampling unit, which may be the enterprise (where responses may be based on financial records) or the establishment (where production units may be recorded separately), also affects the quality of the data. Moreover, much industrial production is organized in unincorporated or owner-operated ventures that are not captured by surveys aimed at the formal sector. Even in large industries, where regular surveys are more likely, evasion of excise and other taxes and nondisclosure of income lower the estimates of value added. Such problems become more acute as countries move from state control of industry to private enterprise, because new firms and growing numbers of established firms fail to report. In accordance with the System of National Accounts, output should include all such unreported activity as well as the value of illegal activities and other unrecorded, informal, or small-scale operations. Data on these activities need to be collected using techniques other than conventional surveys of firms.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.IND.TOTL.ZS (https://data.worldbank.org/indicator/NV.IND.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.IND.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "nv_mnf_chem_zs_un": { "title": "Chemicals (% of value added in manufacturing)", "description_from_producer": "Manufacturing of chemicals and chemical products includes industries classified in ISIC (Rev. 3) division 24. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D).\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIn establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"", "origins": [ { "producer": "International Yearbook of Industrial Statistics - UNIDO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO). Indicator NV.MNF.CHEM.ZS.UN (https://data.worldbank.org/indicator/NV.MNF.CHEM.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.MNF.CHEM.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of value added in manufacturing", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of value added in manufacturing" } }, "nv_mnf_fbto_zs_un": { "title": "Food, beverages and tobacco (% of value added in manufacturing)", "description_from_producer": "Manufacturing of food, beverages, and tobacco includes industries classified in ISIC (Rev. 3) divisions 15 and 16. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D).\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIn establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"", "origins": [ { "producer": "International Yearbook of Industrial Statistics - UNIDO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO). Indicator NV.MNF.FBTO.ZS.UN (https://data.worldbank.org/indicator/NV.MNF.FBTO.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.MNF.FBTO.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of value added in manufacturing", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of value added in manufacturing" } }, "nv_mnf_mtrn_zs_un": { "title": "Machinery and transport equipment (% of value added in manufacturing)", "description_from_producer": "Machinery and transport equipment manufacturing includes industries classified in ISIC (Rev. 3) divisions 29-35. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D).\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIn establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"", "origins": [ { "producer": "International Yearbook of Industrial Statistics - UNIDO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO). Indicator NV.MNF.MTRN.ZS.UN (https://data.worldbank.org/indicator/NV.MNF.MTRN.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.MNF.MTRN.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of value added in manufacturing", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of value added in manufacturing" } }, "nv_mnf_othr_zs_un": { "title": "Other manufacturing (% of value added in manufacturing)", "description_from_producer": "Other manufacturing, a residual, covers wood and related products (ISIC Rev. 3 division 20), paper and related products (ISIC Rev. 3 divisions 21 and 22), petroleum and related products (ISIC Rev. 3 division 23), basic metals and mineral products (ISIC Rev. 3 division27), fabricated metal products and professional goods (ISIC Rev. 3 division 28), and other industries (ISIC Rev. 3 divisions 25, 26, 31, 33, 36, and 37). Includes unallocated data. When data for textiles, machinery, or chemicals are shown as not available, they are included in other manufacturing. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D).\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIn establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"", "origins": [ { "producer": "International Yearbook of Industrial Statistics - UNIDO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO). Indicator NV.MNF.OTHR.ZS.UN (https://data.worldbank.org/indicator/NV.MNF.OTHR.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.MNF.OTHR.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of value added in manufacturing", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of value added in manufacturing" } }, "nv_mnf_tech_zs_un": { "title": "Medium and high-tech manufacturing value added (% manufacturing value added)", "description_from_producer": "The proportion of medium and high-tech industry value added in total value added of manufacturing\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated as the share of the sum of the value added from medium and high-tech industry economic activities to manufacturing value added. The medium and high-tech industry is defined using OECD classification as the following by International Standard Industrial Classification of All Economic Activities (ISIC) Revision 3 and Revision 4 Division respectively: ISIC Rev. 3 (24, 29, 30, 31, 32, 33, 34, 35 excluding 351). Manufacturing value added is the value added of manufacturing industry, which is Section C of ISIC Rev.4, and Section D of ISIC Rev.3. Data can be found in UNIDO INDSTAT4 Database by ISIC Revision 3 and ISIC Revision 4 respectively. Data are collected using General Industrial Statistics Questionnaire which is filled by NSOs and submitted to UNIDO annually. Data for OECD countries are obtained directly from OECD. Country data are also collected from official publications and official web-sites. For additional information please see Table B.2.2 in Appendix B of UNIDO (2017): http://stat.unido.org/content/publications/volume-i%252c-competitive-industrial-performance-report-2016\n\n### Development relevance:\nIndustrial development generally entails a structural transition from resource-based and low technology activities to medium and high-tech industry (MHT) activities. A modern, highly complex production structure offers better opportunities for skills development and technological innovation. MHT activities are also the high value addition industries of manufacturing with higher technological intensity and labour productivity. Increasing the share of MHT sectors also reflects the impact of innovation\n\n### Limitations and exceptions:\nValue added by economic activity should be reported at least at 3-digit ISIC for compiling MHT values. Missing values at country level are imputed based on the methodology from Competitive Industrial Performance Report (UNIDO, 2017. Conversion to USD or difference in ISIC combinations may cause discrepancy between national and international figures. For additional information please see UNIDO (2017): http://stat.unido.org/content/publications/volume-i%252c-competitive-industrial-performance-report-2016", "origins": [ { "producer": "UN Industrial Development Organization (UNIDO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Competitive Industrial Performance (CIP) database, UN Industrial Development Organization (UNIDO). Indicator NV.MNF.TECH.ZS.UN (https://data.worldbank.org/indicator/NV.MNF.TECH.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.MNF.TECH.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "nv_mnf_txtl_zs_un": { "title": "Textiles and clothing (% of value added in manufacturing)", "description_from_producer": "Textiles and clothing refers to industries in ISIC (rev. 3) divisions 17-19 and includes manufacturing of textiles, apparel, dyeing of fur, and tanning of leather. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of value added in manufacturing which is the contribution to the economy by the manufacturing sector (ISIC Rev. 3 major division D).\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIn establishing classifications systems compilers must define both the types of activities to be described and the units whose activities are to be reported. There are many possibilities, and the choices affect how the statistics can be interpreted and how useful they are in analyzing economic behavior. The ISIC emphasizes commonalities in the production process and is explicitly not intended to measure outputs (for which there is a newly developed Central Product Classification). Nevertheless, the ISIC views an activity as defined by \"a process resulting in a homogeneous set of products.\"", "origins": [ { "producer": "International Yearbook of Industrial Statistics - UNIDO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Yearbook of Industrial Statistics, UN Industrial Development Organization (UNIDO). Indicator NV.MNF.TXTL.ZS.UN (https://data.worldbank.org/indicator/NV.MNF.TXTL.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.MNF.TXTL.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of value added in manufacturing", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of value added in manufacturing" } }, "nv_srv_empl_kd": { "title": "Services, value added per worker (constant 2015 US$)", "description_from_producer": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and restaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. The core indicator has been divided by the number of workers in the economy to derive a measure of labor productivity. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Value added per worker is calculated by dividing value added of a sector by the number employed in the sector.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nFor comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output is not easily available, especially in low- and middle-income countries, because the definition, coverage, and methodology are not always consistent across countries. For more details, see Agriculture, forestry, and fishing, value added (constant 2015 US$) [NV.AGR.TOTL.KD], Industry (including construction), value added (constant 2015 US$) [NV.IND.TOTL.KD], and Services, value added (constant 2015 US$) [NV.SRV.TOTL.KD].", "origins": [ { "producer": "World Bank (World Development Indicators) and ILO (ILOSTAT)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Development Indicators database, World Bank (WB);\nILOSTAT database, International Labour Organization (ILO). Indicator NV.SRV.EMPL.KD (https://data.worldbank.org/indicator/NV.SRV.EMPL.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.SRV.EMPL.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "nv_srv_totl_cd": { "title": "Services, value added (current US$)", "description_from_producer": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and restaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.SRV.TOTL.CD (https://data.worldbank.org/indicator/NV.SRV.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.SRV.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "nv_srv_totl_cn": { "title": "Services, value added (current LCU)", "description_from_producer": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and restaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.SRV.TOTL.CN (https://data.worldbank.org/indicator/NV.SRV.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.SRV.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "nv_srv_totl_kd": { "title": "Services, value added (constant 2015 US$)", "description_from_producer": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and restaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defense, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIn the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.SRV.TOTL.KD (https://data.worldbank.org/indicator/NV.SRV.TOTL.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.SRV.TOTL.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "nv_srv_totl_kd_zg": { "title": "Services, value added (annual % growth)", "description_from_producer": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and restaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIn the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.SRV.TOTL.KD.ZG (https://data.worldbank.org/indicator/NV.SRV.TOTL.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.SRV.TOTL.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "nv_srv_totl_kn": { "title": "Services, value added (constant LCU)", "description_from_producer": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and restaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Other notes:\nNote: Data for OECD countries are based on ISIC, revision 4.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.SRV.TOTL.KN (https://data.worldbank.org/indicator/NV.SRV.TOTL.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.SRV.TOTL.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "nv_srv_totl_zs": { "title": "Services, value added (% of GDP)", "description_from_producer": "Services industries correspond to ISIC (Rev. 4) divisions 45-99 and includes wholesale and retail trade, repair of motor vehicles, hotels and restaurants, transport, storage and communication, financial intermediation, real estate, renting and business activities, public administration and defence, compulsory social security, education, health and social work, other community, social and personal service activities, private households with employed persons, and extra-territorial organizations and bodies. Value added is the contribution to the economy by a producer or an industry or an institutional sector, which is estimated by the total value of output produced and deducting the total value of intermediate consumption of goods and services used to produce that output. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to the production approach (or output approach) used to calculate GDP, which gives detailed breakdown of the economy by sectors, providing valuable insights into the structure of an economy and its key drivers of growth. It helps in identifying which sectors are expanding or contracting, information that is crucial for policymakers when designing economic strategies and interventions. Additionally, by focusing on the production side, it reflects the supply conditions of an economy, which can be particularly important when analyzing issues like productivity and competitiveness.\n\n### Limitations and exceptions:\nIn the services industry the many self-employed workers and one-person businesses are sometimes difficult to locate, and they have little incentive to respond to surveys, let alone to report their full earnings. Compounding these problems are the many forms of economic activity that go unrecorded, including the work that women and children do for little or no pay.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NV.SRV.TOTL.ZS (https://data.worldbank.org/indicator/NV.SRV.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NV.SRV.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ny_adj_aedu_cd": { "title": "Adjusted savings: education expenditure (current US$)", "description_from_producer": "Education expenditure refers to the current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank, United Nations Statistics Division, and UNESCO Institute for Statistics", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nStatistical Yearbook, United Nations (UN), publisher: UN Statistics Division;\nOnline database, UN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS). Indicator NY.ADJ.AEDU.CD (https://data.worldbank.org/indicator/NY.ADJ.AEDU.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.AEDU.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_aedu_gn_zs": { "title": "Adjusted savings: education expenditure (% of GNI)", "description_from_producer": "Education expenditure refers to the current operating expenditures in education, including wages and salaries and excluding capital investments in buildings and equipment. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nPublic education expenditures are considered an addition to savings. However, because of the wide variability in the effectiveness of public education expenditures, these figures cannot be construed as the value of investments in human capital. A current expenditure of $1 on education does not necessarily yield $1 of human capital. The calculation should also consider private education expenditure, but data are not available for a large number of countries.", "origins": [ { "producer": "World Bank, United Nations Statistics Division, and UNESCO Institute for Statistics", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nStatistical Yearbook, United Nations (UN), publisher: UN Statistics Division;\nOnline database, UN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS). Indicator NY.ADJ.AEDU.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.AEDU.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.AEDU.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_adj_dco2_cd": { "title": "Adjusted savings: carbon dioxide damage (current US$)", "description_from_producer": "Cost of damage due to carbon dioxide emissions from fossil fuel use and the manufacture of cement, estimated to be US$40 per ton of CO2 (the unit damage in 2017 US dollars for CO2 emitted in 2020) times the number of tons of CO2 emitted. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.DCO2.CD (https://data.worldbank.org/indicator/NY.ADJ.DCO2.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DCO2.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_dco2_gn_zs": { "title": "Adjusted savings: carbon dioxide damage (% of GNI)", "description_from_producer": "Cost of damage due to carbon dioxide emissions from fossil fuel use and the manufacture of cement, estimated to be US$40 per ton of CO2 (the unit damage in 2017 US dollars for CO2 emitted in 2020) times the number of tons of CO2 emitted. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.DCO2.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.DCO2.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DCO2.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_adj_dfor_cd": { "title": "Adjusted savings: net forest depletion (current US$)", "description_from_producer": "Net forest depletion is calculated as the product of unit resource rents and the excess of roundwood harvest over natural growth. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.DFOR.CD (https://data.worldbank.org/indicator/NY.ADJ.DFOR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DFOR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_dfor_gn_zs": { "title": "Adjusted savings: net forest depletion (% of GNI)", "description_from_producer": "Net forest depletion is calculated as the product of unit resource rents and the excess of roundwood harvest over natural growth. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nA positive net depletion figure for forest resources implies that the harvest rate exceeds the rate of natural growth; this is not the same as deforestation, which represents a change in land use. In principle, there should be an addition to savings in countries where growth exceeds harvest, but empirical estimates suggest that most of this net growth is in forested areas that cannot currently be exploited economically. Because the depletion estimates reflect only timber values, they ignore all the external and nontimber benefits associated with standing forests.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.DFOR.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.DFOR.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DFOR.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_adj_dkap_cd": { "title": "Adjusted savings: consumption of fixed capital (current US$)", "description_from_producer": "Consumption of fixed capital represents the replacement value of capital used up in the process of production. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.DKAP.CD (https://data.worldbank.org/indicator/NY.ADJ.DKAP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DKAP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_dkap_gn_zs": { "title": "Adjusted savings: consumption of fixed capital (% of GNI)", "description_from_producer": "Consumption of fixed capital represents the replacement value of capital used up in the process of production. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank and United Nations National Accounts", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nNational Accounts Statistics, United Nations (UN), publisher: UN Statistics Division. Indicator NY.ADJ.DKAP.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.DKAP.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DKAP.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_adj_dmin_cd": { "title": "Adjusted savings: mineral depletion (current US$)", "description_from_producer": "Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.DMIN.CD (https://data.worldbank.org/indicator/NY.ADJ.DMIN.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DMIN.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_dmin_gn_zs": { "title": "Adjusted savings: mineral depletion (% of GNI)", "description_from_producer": "Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.DMIN.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.DMIN.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DMIN.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_adj_dngy_cd": { "title": "Adjusted savings: energy depletion (current US$)", "description_from_producer": "Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.DNGY.CD (https://data.worldbank.org/indicator/NY.ADJ.DNGY.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DNGY.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_dngy_gn_zs": { "title": "Adjusted savings: energy depletion (% of GNI)", "description_from_producer": "Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.DNGY.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.DNGY.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DNGY.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_adj_dpem_cd": { "title": "Adjusted savings: particulate emission damage (current US$)", "description_from_producer": "Particulate emissions damage is the damage due to exposure of a country's population to ambient concentrations of particulates measuring less than 2.5 microns in diameter (PM2.5), ambient ozone pollution, and indoor concentrations of PM2.5 in households cooking with solid fuels. Damages are calculated as foregone labor income due to premature death. Estimates of health impacts from the Global Burden of Disease Study 2013 are for 1990, 1995, 2000, 2005, 2010, and 2013. Data for other years have been extrapolated from trends in mortality rates. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "Institute for Health Metrics and Evaluation (IHME) - Global Burden of Disease 2013, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Burden of Disease 2013 study, Institute for Health Metrics and Evaluation (IHME). Indicator NY.ADJ.DPEM.CD (https://data.worldbank.org/indicator/NY.ADJ.DPEM.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DPEM.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_dpem_gn_zs": { "title": "Adjusted savings: particulate emission damage (% of GNI)", "description_from_producer": "Particulate emissions damage is the damage due to exposure of a country's population to ambient concentrations of particulates measuring less than 2.5 microns in diameter (PM2.5), ambient ozone pollution, and indoor concentrations of PM2.5 in households cooking with solid fuels. Damages are calculated as foregone labor income due to premature death. Estimates of health impacts from the Global Burden of Disease Study 2013 are for 1990, 1995, 2000, 2005, 2010, and 2013. Data for other years have been extrapolated from trends in mortality rates. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nLabor productivity losses, as calculated within the framework of adjusted net savings, represent only part of the economic costs of air pollution and should be interpreted as a lower-end estimate.", "origins": [ { "producer": "Institute for Health Metrics and Evaluation (IHME) - Global Burden of Disease 2013, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Burden of Disease 2013 study, Institute for Health Metrics and Evaluation (IHME). Indicator NY.ADJ.DPEM.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.DPEM.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DPEM.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_adj_dres_gn_zs": { "title": "Adjusted savings: natural resources depletion (% of GNI)", "description_from_producer": "Natural resource depletion is the sum of net forest depletion, energy depletion, and mineral depletion. Net forest depletion is unit resource rents times the excess of roundwood harvest over natural growth. Energy depletion is the ratio of the value of the stock of energy resources to the remaining reserve lifetime (capped at 25 years). It covers coal, crude oil, and natural gas. Mineral depletion is the ratio of the value of the stock of mineral resources to the remaining reserve lifetime (capped at 25 years). It covers tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nNet forest depletion is not the monetary value of deforestation. Roundwood and fuelwood production are different from deforestation, which represents a permanent change in land use and, thus, is not comparable. Areas logged out but intended for regeneration are not included in deforestation figures; rather, they are counted as producing timber depletion. Net forest depletion includes only timber values and does not include the loss of nontimber forest benefits and nonuse benefits.\n\n\n\n\n\n\n\n\n\nFor both energy and mineral depletion, unit resource rent is calculated as (unit world price - average cost) / unit world price. Marginal cost should be used instead of average cost in order to calculate the true opportunity cost of extraction; however, marginal cost is difficult to compute and data are not readily available. Unit prices refer to international rather than local prices to reflect the social cost of natural resources depletion. This differs from methodologies of national accounts, which may use local prices to measure energy or mineral GDP. This difference explains eventual discrepancies in the values for energy or mineral depletion, verses energy or mineral GDP.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.DRES.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.DRES.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.DRES.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_adj_ictr_gn_zs": { "title": "Adjusted savings: gross savings (% of GNI)", "description_from_producer": "Gross savings are the difference between gross national income and public and private consumption, plus net current transfers. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nBecause gross savings is calculated as a residual it includes errors, which may not be offsetting, in its components.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.ADJ.ICTR.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.ICTR.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.ICTR.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_adj_nnat_cd": { "title": "Adjusted savings: net national savings (current US$)", "description_from_producer": "Net national savings are equal to gross national savings less the value of consumption of fixed capital. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.NNAT.CD (https://data.worldbank.org/indicator/NY.ADJ.NNAT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.NNAT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_nnat_gn_zs": { "title": "Adjusted savings: net national savings (% of GNI)", "description_from_producer": "Net national savings are equal to gross national savings less the value of consumption of fixed capital. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.NNAT.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.NNAT.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.NNAT.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_adj_nnty_cd": { "title": "Adjusted net national income (current US$)", "description_from_producer": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nAdjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.NNTY.CD (https://data.worldbank.org/indicator/NY.ADJ.NNTY.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.NNTY.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_nnty_kd": { "title": "Adjusted net national income (constant 2015 US$)", "description_from_producer": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nAdjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.NNTY.KD (https://data.worldbank.org/indicator/NY.ADJ.NNTY.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.NNTY.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "ny_adj_nnty_kd_zg": { "title": "Adjusted net national income (annual % growth)", "description_from_producer": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nAdjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.NNTY.KD.ZG (https://data.worldbank.org/indicator/NY.ADJ.NNTY.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.NNTY.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "ny_adj_nnty_pc_cd": { "title": "Adjusted net national income per capita (current US$)", "description_from_producer": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.NNTY.PC.CD (https://data.worldbank.org/indicator/NY.ADJ.NNTY.PC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.NNTY.PC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_nnty_pc_kd": { "title": "Adjusted net national income per capita (constant 2015 US$)", "description_from_producer": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nAdjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.NNTY.PC.KD (https://data.worldbank.org/indicator/NY.ADJ.NNTY.PC.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.NNTY.PC.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "ny_adj_nnty_pc_kd_zg": { "title": "Adjusted net national income per capita (annual % growth)", "description_from_producer": "Adjusted net national income is GNI minus consumption of fixed capital and natural resources depletion. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nAdjusted net national income differs from the adjustments made in the calculation of adjusted net savings, by not accounting for investments in human capital or the damages from pollution. Thus, adjusted net national income remains within the boundaries of the United Nations System of National Accounts (SNA).\n\n\n\n\n\n\n\n\n\nThe SNA includes non-produced natural assets (such as land, mineral resources, and forests) within the asset boundary when they are under the effective control of institutional units. The calculation of adjusted net national income, which accounts for net forest, energy, and mineral depletion, as well as consumption of fixed capital, thus remains within the SNA boundaries. This point is critical because it allows for comparisons across GDP, GNI, and adjusted net national income; such comparisons reveal the impact of natural resource depletion, which is otherwise ignored by the popular economic indicators.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.NNTY.PC.KD.ZG (https://data.worldbank.org/indicator/NY.ADJ.NNTY.PC.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.NNTY.PC.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual % growth", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual % growth" } }, "ny_adj_svng_cd": { "title": "Adjusted net savings, including particulate emission damage (current US$)", "description_from_producer": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.SVNG.CD (https://data.worldbank.org/indicator/NY.ADJ.SVNG.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.SVNG.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_svng_gn_zs": { "title": "Adjusted net savings, including particulate emission damage (% of GNI)", "description_from_producer": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide and particulate emissions damage. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nThe exercise treats public education expenditures as an addition to savings. However, because of the wide variability in the effectiveness of public education expenditures, these figures cannot be construed as the value of investments in human capital. A current expenditure of $1 on education does not necessarily yield $1 of human capital. The calculation should also consider private education expenditure, but data are not available for a large number of countries.\n\n\n\n\n\n\n\n\n\nWhile extensive, the accounting of natural resource depletion and pollution costs still has some gaps. Key estimates missing on the resource side include the value of fossil water extracted from aquifers, net depletion of fish stocks, and depletion and degradation of soils. Important pollutants affecting human health and economic assets are excluded because no internationally comparable data are widely available on damage from ground-level ozone or sulfur oxides.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.SVNG.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.SVNG.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.SVNG.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_adj_svnx_cd": { "title": "Adjusted net savings, excluding particulate emission damage (current US$)", "description_from_producer": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide. This series excludes particulate emissions damage. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.SVNX.CD (https://data.worldbank.org/indicator/NY.ADJ.SVNX.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.SVNX.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_adj_svnx_gn_zs": { "title": "Adjusted net savings, excluding particulate emission damage (% of GNI)", "description_from_producer": "Adjusted net savings are equal to net national savings plus education expenditure and minus energy depletion, mineral depletion, net forest depletion, and carbon dioxide. This series excludes particulate emissions damage. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank (including The Changing Wealth of Nations)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nThe Changing Wealth of Nations: Measuring Sustainable Development in the New Millennium, World Bank (WB). Indicator NY.ADJ.SVNX.GN.ZS (https://data.worldbank.org/indicator/NY.ADJ.SVNX.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.ADJ.SVNX.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_exp_capm_kn": { "title": "Exports as a capacity to import (constant LCU)", "description_from_producer": "Exports as a capacity to import equals the current price value of exports of goods and services deflated by the import price index. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.EXP.CAPM.KN (https://data.worldbank.org/indicator/NY.EXP.CAPM.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.EXP.CAPM.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ny_gdp_coal_rt_zs": { "title": "Coal rents (% of GDP)", "description_from_producer": "Coal rents are the difference between the value of both hard and soft coal production at world prices and their total costs of production.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP).\n\n### Development relevance:\nAccounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future.", "origins": [ { "producer": "The Changing Wealth of Nations, World Bank staff estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations. Indicator NY.GDP.COAL.RT.ZS (https://data.worldbank.org/indicator/NY.GDP.COAL.RT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.COAL.RT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ny_gdp_defl_kd_zg": { "title": "Inflation, GDP deflator (annual %)", "description_from_producer": "Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.GDP.DEFL.KD.ZG (https://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" } }, "ny_gdp_defl_kd_zg_ad": { "title": "Inflation, GDP deflator: linked series (annual %)", "description_from_producer": "Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank, OECD, and IMF", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GDP.DEFL.KD.ZG.AD (https://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG.AD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG.AD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" } }, "ny_gdp_defl_zs": { "title": "GDP deflator (base year varies by country)", "description_from_producer": "The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. The base year varies by country. This indicator is expressed as a ratio (a\u00f7b).\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.DEFL.ZS (https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "base year varies by country", "display": { "unit": "base year varies by country" } }, "ny_gdp_defl_zs_ad": { "title": "GDP deflator: linked series (base year varies by country)", "description_from_producer": "The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency. The base year varies by country. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed as a ratio (a\u00f7b).\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank, OECD, and IMF", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GDP.DEFL.ZS.AD (https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS.AD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS.AD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "base year varies by country", "display": { "unit": "base year varies by country" } }, "ny_gdp_disc_cn": { "title": "Discrepancy in expenditure estimate of GDP (current LCU)", "description_from_producer": "Although the SNA ensures there is perfect consistency between the three measures of GDP, this is a conceptual consistency that in general does not emerge naturally from data compilations. This is because of the wide disparity of data sources that must be called on and the fact that any error in any source will lead to a difference between at least two of the GDP measures. In practice it is inevitable that many such data errors will exist and will become apparent in exercises such as the balancing of supply and use tables. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.DISC.CN (https://data.worldbank.org/indicator/NY.GDP.DISC.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.DISC.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ny_gdp_disc_kn": { "title": "Discrepancy in expenditure estimate of GDP (constant LCU)", "description_from_producer": "Although the SNA ensures there is perfect consistency between the three measures of GDP, this is a conceptual consistency that in general does not emerge naturally from data compilations. This is because of the wide disparity of data sources that must be called on and the fact that any error in any source will lead to a difference between at least two of the GDP measures. In practice it is inevitable that many such data errors will exist and will become apparent in exercises such as the balancing of supply and use tables. This indicator is expressed in constant prices, meaning the underlying series have been adjusted to account for price changes over time. The reference year for this adjustment varies by country.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.DISC.KN (https://data.worldbank.org/indicator/NY.GDP.DISC.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.DISC.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ny_gdp_fcst_cd": { "title": "Gross value added at basic prices (GVA) (current US$)", "description_from_producer": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.GDP.FCST.CD (https://data.worldbank.org/indicator/NY.GDP.FCST.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.FCST.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_gdp_fcst_cn": { "title": "Gross value added at basic prices (GVA) (current LCU)", "description_from_producer": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.FCST.CN (https://data.worldbank.org/indicator/NY.GDP.FCST.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.FCST.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ny_gdp_fcst_kd": { "title": "Gross value added at basic prices (GVA) (constant 2015 US$)", "description_from_producer": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.FCST.KD (https://data.worldbank.org/indicator/NY.GDP.FCST.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.FCST.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ny_gdp_fcst_kn": { "title": "Gross value added at basic prices (GVA) (constant LCU)", "description_from_producer": "Gross value added at basic prices reflects the price of products receivable by the producer exclusive of taxes payable on products and inclusive of subsidies receivable on products, less intermediate consumption valued at purchasers' prices. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.GDP.FCST.KN (https://data.worldbank.org/indicator/NY.GDP.FCST.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.FCST.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ny_gdp_frst_rt_zs": { "title": "Forest rents (% of GDP)", "description_from_producer": "Forest rents are roundwood harvest times the product of regional prices and a regional rental rate.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP).\n\n### Development relevance:\nAccounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future.", "origins": [ { "producer": "The Changing Wealth of Nations, World Bank staff estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations. Indicator NY.GDP.FRST.RT.ZS (https://data.worldbank.org/indicator/NY.GDP.FRST.RT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.FRST.RT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ny_gdp_minr_rt_zs": { "title": "Mineral rents (% of GDP)", "description_from_producer": "Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP).\n\n### Development relevance:\nAccounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future.", "origins": [ { "producer": "The Changing Wealth of Nations, World Bank staff estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations. Indicator NY.GDP.MINR.RT.ZS (https://data.worldbank.org/indicator/NY.GDP.MINR.RT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.MINR.RT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ny_gdp_mktp_cd": { "title": "GDP (current US$)", "description_from_producer": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nGross domestic product (GDP), though widely tracked, may not always be the most relevant summary of aggregated economic performance for all economies, especially when production occurs at the expense of consuming capital stock.\n\n\n\n\n\n\n\n\n\nWhile GDP estimates based on the production approach are generally more reliable than estimates compiled from the income or expenditure side, different countries use different definitions, methods, and reporting standards. World Bank staff review the quality of national accounts data and sometimes make adjustments to improve consistency with international guidelines. Nevertheless, significant discrepancies remain between international standards and actual practice. Many statistical offices, especially those in developing countries, face severe limitations in the resources, time, training, and budgets required to produce reliable and comprehensive series of national accounts statistics.\n\n\n\n\n\n\n\n\n\nAmong the difficulties faced by compilers of national accounts is the extent of unreported economic activity in the informal or secondary economy. In developing countries a large share of agricultural output is either not exchanged (because it is consumed within the household) or not exchanged for money.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.MKTP.CD (https://data.worldbank.org/indicator/NY.GDP.MKTP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.MKTP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_gdp_mktp_cn": { "title": "GDP (current LCU)", "description_from_producer": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.MKTP.CN (https://data.worldbank.org/indicator/NY.GDP.MKTP.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.MKTP.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ny_gdp_mktp_cn_ad": { "title": "GDP: linked series (current LCU)", "description_from_producer": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank, OECD, and IMF", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GDP.MKTP.CN.AD (https://data.worldbank.org/indicator/NY.GDP.MKTP.CN.AD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.MKTP.CN.AD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ny_gdp_mktp_kd": { "title": "GDP (constant 2015 US$)", "description_short": "Total economic output of a country or region per year. This data is adjusted by inflation but _does not_ account for differences in living costs between countries.", "description_from_producer": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nEach industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\n\n\n\n\n\n\n\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\n\n\n\n\n\n\n\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\n\n\n\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\n\n\n\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\n\n\n\n\n\n\n\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate.", "description_key": "- [Gross domestic product](#dod:gdp) (GDP) is a comprehensive measure of a country's total income. It helps compare the size of economies and track how they change over time. It is especially useful for understanding trends in economic growth.\n- The GDP is calculated as the value of all final goods and services produced each year in a country. It represents the total economic output of a country or region.\n- This GDP indicator provides information on economic growth and income levels from 1960.\n- This data is adjusted by inflation but _does not_ account for differences in living costs between countries.\n- This data is expressed in constant 2015 US$.\n- For GDP estimates that account for both inflation and differences in the cost of living between countries, see [GDP in constant international-$](https://ourworldindata.org/grapher/gdp-worldbank).", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.MKTP.KD (https://data.worldbank.org/indicator/NY.GDP.MKTP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.MKTP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "name": "GDP", "numDecimalPlaces": 0, "tolerance": 5, "shortUnit": "$", "unit": "constant 2015 US$" }, "processing_level": "minor", "presentation": { "grapher_config": { "title": "Gross domestic product (GDP)", "subtitle": "This data is expressed in US dollars. It is adjusted for inflation but does not account for differences in living costs between countries.", "note": "This data is expressed in constant 2015 US$.", "variantName": "In constant US$ - World Bank", "originUrl": "https://ourworldindata.org/economic-growth", "map": { "colorScale": { "binningStrategy": "manual", "baseColorScheme": "PuBu", "customNumericValues": [ 0, 10000000000, 30000000000, 100000000000, 300000000000, 1000000000000, 3000000000000, 10000000000000, 1 ] } }, "selectedEntityNames": [ "United States", "Germany", "United Kingdom", "Brazil", "South Korea", "Japan", "China", "India", "Ethiopia", "Russia" ], "yAxis": { "canChangeScaleType": true, "min": 0 }, "tab": "map", "hideRelativeToggle": false, "hasMapTab": true }, "title_public": "Gross domestic product (GDP)", "title_variant": "In constant US$", "attribution_short": "World Bank", "topic_tags": [ "Economic Growth" ] } }, "ny_gdp_mktp_kd_zg": { "title": "GDP growth (annual %)", "description_from_producer": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.\n\n### Limitations and exceptions:\nEach industry's contribution to growth in the economy's output is measured by growth in the industry's value added. In principle, value added in constant prices can be estimated by measuring the quantity of goods and services produced in a period, valuing them at an agreed set of base year prices, and subtracting the cost of intermediate inputs, also in constant prices. This double-deflation method requires detailed information on the structure of prices of inputs and outputs.\n\n\n\n\n\n\n\n\n\nIn many industries, however, value added is extrapolated from the base year using single volume indexes of outputs or, less commonly, inputs. Particularly in the services industries, including most of government, value added in constant prices is often imputed from labor inputs, such as real wages or number of employees. In the absence of well defined measures of output, measuring the growth of services remains difficult.\n\n\n\n\n\n\n\n\n\nMoreover, technical progress can lead to improvements in production processes and in the quality of goods and services that, if not properly accounted for, can distort measures of value added and thus of growth. When inputs are used to estimate output, as for nonmarket services, unmeasured technical progress leads to underestimates of the volume of output. Similarly, unmeasured improvements in quality lead to underestimates of the value of output and value added. The result can be underestimates of growth and productivity improvement and overestimates of inflation.\n\n\n\n\n\n\n\n\n\nInformal economic activities pose a particular measurement problem, especially in developing countries, where much economic activity is unrecorded. A complete picture of the economy requires estimating household outputs produced for home use, sales in informal markets, barter exchanges, and illicit or deliberately unreported activities. The consistency and completeness of such estimates depend on the skill and methods of the compiling statisticians.\n\n\n\n\n\n\n\n\n\nRebasing of national accounts can alter the measured growth rate of an economy and lead to breaks in series that affect the consistency of data over time. When countries rebase their national accounts, they update the weights assigned to various components to better reflect current patterns of production or uses of output. The new base year should represent normal operation of the economy - it should be a year without major shocks or distortions. Some developing countries have not rebased their national accounts for many years. Using an old base year can be misleading because implicit price and volume weights become progressively less relevant and useful.\n\n\n\n\n\n\n\n\n\nTo obtain comparable series of constant price data for computing aggregates, the World Bank rescales GDP and value added by industrial origin to a common reference year. Because rescaling changes the implicit weights used in forming regional and income group aggregates, aggregate growth rates are not comparable with those from earlier editions with different base years. Rescaling may result in a discrepancy between the rescaled GDP and the sum of the rescaled components. To avoid distortions in the growth rates, the discrepancy is left unallocated. As a result, the weighted average of the growth rates of the components generally does not equal the GDP growth rate.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.MKTP.KD.ZG (https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" } }, "ny_gdp_mktp_kn": { "title": "GDP (constant LCU)", "description_from_producer": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.MKTP.KN (https://data.worldbank.org/indicator/NY.GDP.MKTP.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.MKTP.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ny_gdp_mktp_pp_cd": { "title": "GDP, PPP (current international $)", "description_from_producer": "This indicator provides values for gross domestic product (GDP) expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world's countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations' databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "International Comparison Program (ICP), Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP\u2019s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat, date accessed: Periodical update;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-https://data-explorer.oecd.org/, publisher: OECD, date accessed: Periodical update;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GDP.MKTP.PP.CD (https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current international $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current international $" } }, "ny_gdp_mktp_pp_kd": { "title": "GDP, PPP (constant 2021 international $)", "description_short": "Total economic output of a country or region per year. This data is adjusted for inflation and differences in living costs between countries.", "description_from_producer": "This indicator provides values for gross domestic product (GDP) expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world\u2019s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations\u2019 databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "description_key": "- [Gross domestic product](#dod:gdp) (GDP) is a comprehensive measure of a country's total income. It helps compare the size of economies and track how they change over time. It is especially useful for understanding trends in economic growth.\n- The GDP is calculated as the value of all final goods and services produced each year in a country. It represents the total economic output of a country or region.\n- This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)\n- This data comes from the World Bank and starts in 1990. For estimates going back several centuries, explore our chart of GDP from the [Maddison Project Database](https://ourworldindata.org/grapher/gdp-maddison-project-database).", "origins": [ { "producer": "Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP\u2019s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GDP.MKTP.PP.KD (https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "international-$ in 2021 prices", "short_unit": "$", "display": { "name": "GDP", "numDecimalPlaces": 0, "tolerance": 5, "shortUnit": "$", "unit": "international-$ in 2021 prices" }, "processing_level": "minor", "presentation": { "grapher_config": { "title": "Gross domestic product (GDP)", "subtitle": "This data is adjusted for inflation and differences in living costs between countries.", "note": "This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2021 prices.", "hideRelativeToggle": false, "hasMapTab": true, "tab": "map", "variantName": "In constant international-$ \u2013 World Bank", "originUrl": "https://ourworldindata.org/economic-growth", "yAxis": { "min": 0, "canChangeScaleType": true }, "map": { "colorScale": { "baseColorScheme": "PuBu", "binningStrategy": "manual", "customNumericValues": [ 0, 10000000000, 30000000000, 100000000000, 300000000000, 1000000000000, 3000000000000, 10000000000000, 1 ] } }, "selectedEntityNames": [ "United States", "Germany", "United Kingdom", "Brazil", "South Korea", "Japan", "China", "India", "Ethiopia", "Russia" ] }, "title_public": "Gross domestic product (GDP)", "title_variant": "In constant international-$", "attribution_short": "World Bank", "topic_tags": [ "Economic Growth" ], "faqs": [ { "gdoc_id": "1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw", "fragment_id": "poverty-international-dollars" } ] } }, "ny_gdp_ngas_rt_zs": { "title": "Natural gas rents (% of GDP)", "description_from_producer": "Natural gas rents are the difference between the value of natural gas production at regional prices and total costs of production.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP).\n\n### Development relevance:\nAccounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future.", "origins": [ { "producer": "The Changing Wealth of Nations, World Bank staff estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations. Indicator NY.GDP.NGAS.RT.ZS (https://data.worldbank.org/indicator/NY.GDP.NGAS.RT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.NGAS.RT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ny_gdp_pcap_cd": { "title": "GDP per capita (current US$)", "description_from_producer": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.PCAP.CD (https://data.worldbank.org/indicator/NY.GDP.PCAP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.PCAP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" }, "presentation": { "topic_tags": [ "Economic Growth" ] } }, "ny_gdp_pcap_cn": { "title": "GDP per capita (current LCU)", "description_from_producer": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.PCAP.CN (https://data.worldbank.org/indicator/NY.GDP.PCAP.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.PCAP.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" }, "presentation": { "topic_tags": [ "Economic Growth" ] } }, "ny_gdp_pcap_kd": { "title": "GDP per capita (constant 2015 US$)", "description_short": "Average economic output per person in a country or region per year. This data is adjusted by inflation but _does not_ account for differences in living costs between countries.", "description_from_producer": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "description_key": "- Gross domestic product (GDP) is a measure of the total value added from the production of goods and services in a country or region each year. GDP per capita is GDP divided by population.\n- This GDP per capita indicator provides information on economic growth and income levels from 1960.\n- This data is adjusted by inflation but _does not_ account for differences in living costs between countries.\n- This data is expressed in constant 2015 US$.\n- For GDP per capita estimates that account for both inflation and differences in the cost of living between countries, see [GDP per capita in constant international-$](https://ourworldindata.org/grapher/gdp-per-capita-worldbank).", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.PCAP.KD (https://data.worldbank.org/indicator/NY.GDP.PCAP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.PCAP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "name": "GDP per capita", "numDecimalPlaces": 0, "tolerance": 5, "shortUnit": "$", "unit": "constant 2015 US$" }, "processing_level": "minor", "presentation": { "grapher_config": { "title": "GDP per capita", "subtitle": "This data is expressed in US dollars. It is adjusted for inflation but does not account for differences in living costs between countries.", "note": "This data is expressed in constant 2015 US$.", "variantName": "In constant US$ - World Bank", "originUrl": "https://ourworldindata.org/economic-growth", "map": { "colorScale": { "binningStrategy": "manual", "baseColorScheme": "GnBu", "customNumericValues": [ 0, 1000, 2000, 5000, 10000, 20000, 50000, 1 ] } }, "selectedEntityNames": [ "United States", "Germany", "United Kingdom", "Brazil", "South Korea", "Japan", "China", "India", "Ethiopia", "Russia" ] }, "title_public": "GDP per capita", "title_variant": "In constant US$", "attribution_short": "World Bank", "topic_tags": [ "Economic Growth" ] } }, "ny_gdp_pcap_kd_zg": { "title": "GDP per capita growth (annual %)", "description_from_producer": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.PCAP.KD.ZG (https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.PCAP.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" }, "presentation": { "topic_tags": [ "Economic Growth" ] } }, "ny_gdp_pcap_kn": { "title": "GDP per capita (constant LCU)", "description_from_producer": "Gross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.PCAP.KN (https://data.worldbank.org/indicator/NY.GDP.PCAP.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.PCAP.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" }, "presentation": { "topic_tags": [ "Economic Growth" ] } }, "ny_gdp_pcap_pp_cd": { "title": "GDP per capita, PPP (current international $)", "description_from_producer": "This indicator provides values for gross domestic product (GDP) per person expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world\u2019s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations\u2019 databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP\u2019s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GDP.PCAP.PP.CD (https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current international $", "short_unit": "$", "display": { "name": "GDP per capita", "numDecimalPlaces": 0, "shortUnit": "$", "unit": "current international $" }, "presentation": { "title_public": "GDP per capita", "title_variant": "In current international $", "topic_tags": [ "Economic Growth" ] } }, "ny_gdp_pcap_pp_kd": { "title": "GDP per capita, PPP (constant 2021 international $)", "description_short": "Average economic output per person in a country or region per year. This data is adjusted for inflation and differences in living costs between countries.", "description_from_producer": "This indicator provides values for gross domestic product (GDP) per person expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\n\nGross domestic product is the total income earned through the production of goods and services in an economic territory during an accounting period. It can be measured in three different ways: using either the expenditure approach, the income approach, or the production approach. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world\u2019s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations\u2019 databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "description_key": "- GDP per capita is a comprehensive measure of people's average income. It helps compare income levels across countries and track how they change over time. It is especially useful for understanding trends in economic growth and living standards.\n- GDP per capita is calculated as the value of all final goods and services produced each year in a country (the [gross domestic product](#dod:gdp)), divided by the population. It represents the average economic output per person.\n- This indicator shows the large inequality between people in different countries. In the poorest countries, average incomes are below $1,000 per year; in rich countries, they are more than 50 times higher.\n- This data is expressed in constant international dollars to adjust for inflation and differences in living costs between countries. Read more in our article, [What are international dollars?](https://ourworldindata.org/international-dollars)\n- This data comes from the World Bank and starts in 1990. For estimates going back several centuries, explore our chart of GDP per capita from the [Maddison Project Database](https://ourworldindata.org/grapher/gdp-per-capita-maddison-project-database).", "origins": [ { "producer": "Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP\u2019s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GDP.PCAP.PP.KD (https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "international-$ in 2021 prices", "short_unit": "$", "display": { "name": "GDP per capita", "numDecimalPlaces": 0, "tolerance": 5, "shortUnit": "$", "unit": "international-$ in 2021 prices" }, "processing_level": "minor", "presentation": { "grapher_config": { "title": "GDP per capita", "subtitle": "GDP per capita is a country's [gross domestic product](#dod:gdp) divided by its population. This data is adjusted for inflation and differences in living costs between countries.", "note": "This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2021 prices.", "variantName": "In constant international-$ \u2013 World Bank", "originUrl": "https://ourworldindata.org/economic-growth", "hasMapTab": true, "tab": "map", "yAxis": { "min": 0, "canChangeScaleType": true }, "hideAnnotationFieldsInTitle": { "time": true, "entity": true, "changeInPrefix": true }, "hideRelativeToggle": false, "map": { "colorScale": { "baseColorScheme": "GnBu", "binningStrategy": "manual", "customNumericValues": [ 0, 1000, 2000, 5000, 10000, 20000, 50000, 1 ] } }, "selectedEntityNames": [ "United States", "Germany", "United Kingdom", "Brazil", "South Korea", "Japan", "China", "India", "Ethiopia", "Russia" ] }, "title_public": "GDP per capita", "title_variant": "In constant international-$", "attribution_short": "World Bank", "topic_tags": [ "Economic Growth" ], "faqs": [ { "gdoc_id": "1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw", "fragment_id": "poverty-international-dollars" } ] } }, "ny_gdp_petr_rt_zs": { "title": "Oil rents (% of GDP)", "description_from_producer": "Oil rents are the difference between the value of crude oil production at regional prices and total costs of production.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP).\n\n### Development relevance:\nAccounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future.", "origins": [ { "producer": "The Changing Wealth of Nations, World Bank staff estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations. Indicator NY.GDP.PETR.RT.ZS (https://data.worldbank.org/indicator/NY.GDP.PETR.RT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.PETR.RT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ny_gdp_totl_rt_zs": { "title": "Total natural resources rents (% of GDP)", "description_from_producer": "Total natural resources rents are the sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates of natural resources rents are calculated as the difference between the price of a commodity and the average cost of producing it. This is done by estimating the price of units of specific commodities and subtracting estimates of average unit costs of extraction or harvesting costs. These unit rents are then multiplied by the physical quantities countries extract or harvest to determine the rents for each commodity as a share of gross domestic product (GDP).\n\n### Development relevance:\nAccounting for the contribution of natural resources to economic output is important in building an analytical framework for sustainable development. In some countries earnings from natural resources, especially from fossil fuels and minerals, account for a sizable share of GDP, and much of these earnings come in the form of economic rents - revenues above the cost of extracting the resources.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNatural resources give rise to economic rents because they are not produced. For produced goods and services competitive forces expand supply until economic profits are driven to zero, but natural resources in fixed supply often command returns well in excess of their cost of production. Rents from nonrenewable resources - fossil fuels and minerals - as well as rents from overharvesting of forests indicate the liquidation of a country's capital stock. When countries use such rents to support current consumption rather than to invest in new capital to replace what is being used up, they are, in effect, borrowing against their future.", "origins": [ { "producer": "The Changing Wealth of Nations, World Bank staff estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Changing Wealth of Nations, World Bank (WB), uri: https://www.worldbank.org/en/publication/changing-wealth-of-nations/data, note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations., publisher: World Bank (WB);\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on sources and methods described in the World Bank's The Changing Wealth of Nations. Indicator NY.GDP.TOTL.RT.ZS (https://data.worldbank.org/indicator/NY.GDP.TOTL.RT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.TOTL.RT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ny_gds_totl_cd": { "title": "Gross domestic savings (current US$)", "description_from_producer": "Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDS.TOTL.CD (https://data.worldbank.org/indicator/NY.GDS.TOTL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDS.TOTL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_gds_totl_cn": { "title": "Gross domestic savings (current LCU)", "description_from_producer": "Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDS.TOTL.CN (https://data.worldbank.org/indicator/NY.GDS.TOTL.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDS.TOTL.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ny_gds_totl_zs": { "title": "Gross domestic savings (% of GDP)", "description_from_producer": "Gross domestic savings are calculated as GDP less final consumption expenditure (total consumption). This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDS.TOTL.ZS (https://data.worldbank.org/indicator/NY.GDS.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDS.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ny_gdy_totl_kn": { "title": "Gross domestic income (constant LCU)", "description_from_producer": "Real gross domestic income (real GDI) measures the purchasing power of the total incomes generated by domestic production. It is a concept that exists in real terms only. When the terms of trade change there may be a significant divergence between the movements of GDP in volume terms and real GDI. The difference between the change in GDP in volume terms and real GDI is generally described as the \u201ctrading gain\u201d (or loss) or, to turn this round, the trading gain or loss from changes in the terms of trade is the difference between real GDI and GDP in volume terms. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDY.TOTL.KN (https://data.worldbank.org/indicator/NY.GDY.TOTL.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDY.TOTL.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ny_gnp_atls_cd": { "title": "GNI, Atlas method (current US$)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This figure is converted to U.S. dollars using the World Bank Atlas method. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GNP.ATLS.CD (https://data.worldbank.org/indicator/NY.GNP.ATLS.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.ATLS.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_gnp_mktp_cd": { "title": "GNI (current US$)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GNP.MKTP.CD (https://data.worldbank.org/indicator/NY.GNP.MKTP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.MKTP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_gnp_mktp_cn": { "title": "GNI (current LCU)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GNP.MKTP.CN (https://data.worldbank.org/indicator/NY.GNP.MKTP.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.MKTP.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ny_gnp_mktp_cn_ad": { "title": "GNI: linked series (current LCU)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "World Bank, OECD, and IMF", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GNP.MKTP.CN.AD (https://data.worldbank.org/indicator/NY.GNP.MKTP.CN.AD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.MKTP.CN.AD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "ny_gnp_mktp_kd": { "title": "GNI (constant 2015 US$)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GNP.MKTP.KD (https://data.worldbank.org/indicator/NY.GNP.MKTP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.MKTP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "ny_gnp_mktp_kd_zg": { "title": "GNI growth (annual %)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GNP.MKTP.KD.ZG (https://data.worldbank.org/indicator/NY.GNP.MKTP.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.MKTP.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" } }, "ny_gnp_mktp_kn": { "title": "GNI (constant LCU)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GNP.MKTP.KN (https://data.worldbank.org/indicator/NY.GNP.MKTP.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.MKTP.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ny_gnp_mktp_pp_cd": { "title": "GNI, PPP (current international $)", "description_from_producer": "This indicator provides values for gross national income (GNI) expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world's countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPPs are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations' databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP\u2019s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GNP.MKTP.PP.CD (https://data.worldbank.org/indicator/NY.GNP.MKTP.PP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.MKTP.PP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current international $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current international $" } }, "ny_gnp_mktp_pp_kd": { "title": "GNI, PPP (constant 2021 international $)", "description_short": "Total income earned by residents of a country or region, including income earned abroad. This data is adjusted for inflation and differences in living costs between countries.", "description_from_producer": "This indicator provides values for gross national income (GNI) expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world\u2019s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations\u2019 databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "description_key": "- Gross national income (GNI) is a measure of the total income earned by residents of a country or region each year. It is calculated as GDP plus net income received from abroad, plus taxes (minus subsidies) on production.\n- This GNI indicator provides information on economic growth and income levels from 1990.\n- This data is adjusted for inflation and differences in living costs between countries.\n- This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2021 prices.", "origins": [ { "producer": "Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP\u2019s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GNP.MKTP.PP.KD (https://data.worldbank.org/indicator/NY.GNP.MKTP.PP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.MKTP.PP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "international-$ in 2021 prices", "short_unit": "$", "display": { "name": "GNI", "numDecimalPlaces": 0, "tolerance": 5, "shortUnit": "$", "unit": "international-$ in 2021 prices" }, "processing_level": "minor", "presentation": { "title_public": "Gross national income (GNI)", "title_variant": "In constant international-$", "attribution_short": "World Bank", "topic_tags": [ "Economic Growth" ], "faqs": [ { "gdoc_id": "1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw", "fragment_id": "poverty-international-dollars" } ] } }, "ny_gnp_pcap_cd": { "title": "GNI per capita, Atlas method (current US$)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. This figure is converted to U.S. dollars using the World Bank Atlas method, and divided by the midyear population. GNI, calculated in national currency, is usually converted to U.S. dollars at official exchange rates for comparisons across economies, although an alternative rate is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate actually applied in international transactions. To smooth fluctuations in prices and exchange rates, a special Atlas method of conversion is used by the World Bank. This applies a conversion factor that averages the exchange rate for a given year and the two preceding years, adjusted for differences in rates of inflation between the country, and through 2000, the G-5 countries (France, Germany, Japan, the United Kingdom, and the United States). From 2001, these countries include the Euro area, Japan, the United Kingdom, and the United States. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank's official estimates of the size of economies and country classifications by income level are based on Gross National Income (GNI) per capita. For cross-national comparisons, estimates are converted from local currency units (LCU) to current U.S. dollars using the Atlas method, referring to a former World Bank publication called the Atlas of Global Development. The Atlas method smooths exchange rate fluctuations using a three-year moving average, price-adjusted conversion factor. The USD estimate of GNI per capita is derived by applying the Atlas conversion factor to estimates measured in LCU.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GNP.PCAP.CD (https://data.worldbank.org/indicator/NY.GNP.PCAP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.PCAP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_gnp_pcap_cn": { "title": "GNI per capita (current LCU)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicators of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.GNP.PCAP.CN (https://data.worldbank.org/indicator/NY.GNP.PCAP.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.PCAP.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ny_gnp_pcap_kd": { "title": "GNI per capita (constant 2015 US$)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2015. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GNP.PCAP.KD (https://data.worldbank.org/indicator/NY.GNP.PCAP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.PCAP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2015 US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "constant 2015 US$" } }, "ny_gnp_pcap_kd_zg": { "title": "GNI per capita growth (annual %)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator denotes the percentage change over each previous year of the constant price (base year 2015) series in United States dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GNP.PCAP.KD.ZG (https://data.worldbank.org/indicator/NY.GNP.PCAP.KD.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.PCAP.KD.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" } }, "ny_gnp_pcap_kn": { "title": "GNI per capita (constant LCU)", "description_from_producer": "Gross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate.This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Per capita estimates are divided by the total population.\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GNP.PCAP.KN (https://data.worldbank.org/indicator/NY.GNP.PCAP.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.PCAP.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ny_gnp_pcap_pp_cd": { "title": "GNI per capita, PPP (current international $)", "description_from_producer": "This indicator provides values for gross national income (GNI) per person expressed in current international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate. This series has been linked to produce a consistent time series to counteract breaks in series over time due to changes in base years, source data and methodologies. Thus, it may not be comparable with other national accounts series in the database for historical years. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world's countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPPs are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations' databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP\u2019s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GNP.PCAP.PP.CD (https://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current international $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current international $" } }, "ny_gnp_pcap_pp_kd": { "title": "GNI per capita, PPP (constant 2021 international $)", "description_short": "Average income per person earned by residents of a country or region, including income earned abroad. This data is adjusted for inflation and differences in living costs between countries.", "description_from_producer": "This indicator provides values for gross national income (GNI) per person expressed in constant international dollars, converted by purchasing power parities (PPPs). PPPs account for the different price levels across countries and thus PPP-based comparisons of economic output are more appropriate for comparing the output of economies and the average material well-being of their inhabitants than exchange-rate based comparisons.\n\nGross national income is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad. The core indicator has been divided by the general population to achieve a per capita estimate. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment is 2021. The PPP conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of GDP and its expenditure components.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world's countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model. ICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for earlier years, and 2021 PPPs for later years. For the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations' databases.\n\nNational accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/ Linked series have been smoothed to remove breaks resulting from changes in base years, data sources or compilation methods. The linking is performed using historical nominal growth rates from archived WDI databases.\n\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates. PPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures.\n\nThe conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nThe PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "description_key": "- Gross national income (GNI) is a measure of the total income earned by residents of a country or region each year. It is calculated as GDP plus net income received from abroad, plus taxes (minus subsidies) on production. GNI per capita is GNI divided by population.\n- This GNI per capita indicator provides information on economic growth and income levels from 1990.\n- This data is adjusted for inflation and differences in living costs between countries.\n- This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2021 prices.", "origins": [ { "producer": "Eurostat, OECD, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, note: This information is for ICP\u2019s PPPs utilized in WDI, publisher: International Comparison Program (ICP), date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-explorer.oecd.org/, publisher: OECD;\nStaff estimates, World Bank (WB);\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nWorld Economic Outlook database, International Monetary Fund (IMF). Indicator NY.GNP.PCAP.PP.KD (https://data.worldbank.org/indicator/NY.GNP.PCAP.PP.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNP.PCAP.PP.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "international-$ in 2021 prices", "short_unit": "$", "display": { "name": "GNI per capita", "numDecimalPlaces": 0, "tolerance": 5, "shortUnit": "$", "unit": "international-$ in 2021 prices" }, "processing_level": "minor", "presentation": { "grapher_config": { "title": "Gross national income (GNI) per capita", "subtitle": "Gross national income (GNI) measures the total income earned by residents of a country, including income earned abroad. This data is adjusted for inflation and differences in living costs between countries.", "note": "This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2021 prices.", "variantName": "World Bank", "originUrl": "https://ourworldindata.org/economic-growth", "map": { "colorScale": { "binningStrategy": "manual", "baseColorScheme": "BuGn", "customNumericValues": [ 0, 1000, 2000, 5000, 10000, 20000, 50000, 1 ] } }, "selectedEntityNames": [ "United States", "Germany", "United Kingdom", "Brazil", "South Korea", "Japan", "China", "India", "Ethiopia", "Russia" ], "yAxis": { "canChangeScaleType": true, "min": 0 }, "tab": "map", "hideRelativeToggle": false, "hasMapTab": true }, "title_public": "Gross national income (GNI) per capita", "title_variant": "In constant international-$", "attribution_short": "World Bank", "topic_tags": [ "Economic Growth" ], "faqs": [ { "gdoc_id": "1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw", "fragment_id": "poverty-international-dollars" } ] } }, "ny_gns_ictr_cd": { "title": "Gross savings (current US$)", "description_from_producer": "Savings is an amount that represents the part of disposable income (adjusted for the\n\n\n\n\n\n\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicators of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.GNS.ICTR.CD (https://data.worldbank.org/indicator/NY.GNS.ICTR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNS.ICTR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_gns_ictr_cn": { "title": "Gross savings (current LCU)", "description_from_producer": "Savings is an amount that represents the part of disposable income (adjusted for the\n\n\n\n\n\n\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicators of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.GNS.ICTR.CN (https://data.worldbank.org/indicator/NY.GNS.ICTR.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNS.ICTR.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ny_gns_ictr_gn_zs": { "title": "Gross savings (% of GNI)", "description_from_producer": "Savings is an amount that represents the part of disposable income (adjusted for the\n\n\n\n\n\n\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed as a percentage of Gross National Income (GNI) which is the total income earned by all residents within an economic territory during an accounting period. It is equal to gross domestic product plus earned income receivable from abroad minus earned income payable abroad.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicators of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.GNS.ICTR.GN.ZS (https://data.worldbank.org/indicator/NY.GNS.ICTR.GN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNS.ICTR.GN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GNI", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GNI" } }, "ny_gns_ictr_zs": { "title": "Gross savings (% of GDP)", "description_from_producer": "Savings is an amount that represents the part of disposable income (adjusted for the\n\n\n\n\n\n\n\n\nchange in pension entitlements) that is not spent on final consumption. Gross savings are calculated as gross national income less total consumption, plus net transfers. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.GNS.ICTR.ZS (https://data.worldbank.org/indicator/NY.GNS.ICTR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GNS.ICTR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "ny_gsr_nfcy_cd": { "title": "Net primary income (Net income from abroad) (current US$)", "description_from_producer": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the primary income account of the balance of payments, which shows amounts payable and receivable in return for providing temporary use to another entity of labor, financial resources, or nonproduced nonfinancial assets. This account can help in understanding the sustainability of a country's current account balance, assessing the country's ability to finance its foreign payments, and analyzing the income generated from foreign assets compared to the payments made to foreign investors. Overall, it provides valuable insights into the flow of income across borders, which is crucial for economic analysis and policymaking.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.GSR.NFCY.CD (https://data.worldbank.org/indicator/NY.GSR.NFCY.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GSR.NFCY.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_gsr_nfcy_cn": { "title": "Net primary income (Net income from abroad) (current LCU)", "description_from_producer": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the primary income account of the balance of payments, which shows amounts payable and receivable in return for providing temporary use to another entity of labor, financial resources, or nonproduced nonfinancial assets. This account can help in understanding the sustainability of a country's current account balance, assessing the country's ability to finance its foreign payments, and analyzing the income generated from foreign assets compared to the payments made to foreign investors. Overall, it provides valuable insights into the flow of income across borders, which is crucial for economic analysis and policymaking.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.GSR.NFCY.CN (https://data.worldbank.org/indicator/NY.GSR.NFCY.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GSR.NFCY.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ny_gsr_nfcy_kn": { "title": "Net primary income (Net income from abroad) (constant LCU)", "description_from_producer": "Net primary income includes the net labor income and net property and entrepreneurial income components of the SNA. Labor income covers compensation of employees paid to nonresident workers. Property and entrepreneurial income covers investment income from the ownership of foreign financial claims (interest, dividends, rent, etc.) and nonfinancial property income (patents, copyrights, etc.). This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the primary income account of the balance of payments, which shows amounts payable and receivable in return for providing temporary use to another entity of labor, financial resources, or nonproduced nonfinancial assets. This account can help in understanding the sustainability of a country's current account balance, assessing the country's ability to finance its foreign payments, and analyzing the income generated from foreign assets compared to the payments made to foreign investors. Overall, it provides valuable insights into the flow of income across borders, which is crucial for economic analysis and policymaking.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.GSR.NFCY.KN (https://data.worldbank.org/indicator/NY.GSR.NFCY.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GSR.NFCY.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ny_tax_nind_cd": { "title": "Taxes less subsidies on products (current US$)", "description_from_producer": "Taxes less subsidies on products are taxes payable per unit of a good or service. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.TAX.NIND.CD (https://data.worldbank.org/indicator/NY.TAX.NIND.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.TAX.NIND.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_tax_nind_cn": { "title": "Taxes less subsidies on products (current LCU)", "description_from_producer": "Taxes less subsidies on products are taxes payable per unit of a good or service. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.TAX.NIND.CN (https://data.worldbank.org/indicator/NY.TAX.NIND.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.TAX.NIND.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ny_tax_nind_kn": { "title": "Taxes less subsidies on products (constant LCU)", "description_from_producer": "Taxes less subsidies on products are taxes payable per unit of a good or service. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.TAX.NIND.KN (https://data.worldbank.org/indicator/NY.TAX.NIND.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.TAX.NIND.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ny_trf_nctr_cd": { "title": "Net secondary income (Net current transfers from abroad) (current US$)", "description_from_producer": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.TRF.NCTR.CD (https://data.worldbank.org/indicator/NY.TRF.NCTR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.TRF.NCTR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_trf_nctr_cn": { "title": "Net secondary income (Net current transfers from abroad) (current LCU)", "description_from_producer": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.TRF.NCTR.CN (https://data.worldbank.org/indicator/NY.TRF.NCTR.CN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.TRF.NCTR.CN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current LCU", "display": { "unit": "current LCU" } }, "ny_trf_nctr_kn": { "title": "Net secondary income (Net current transfers from abroad) (constant LCU)", "description_from_producer": "Net secondary income (from abroad) comprises transfers of income between residents of the reporting country and the rest of the world that carry no provisions for repayment. Net secondary income is equal to the unrequited transfers of income from nonresidents to residents minus the unrequited transfers from residents to nonresidents. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the balance of payments, which is a statement that summarizes economic transactions between residents and nonresidents during a specific time period. It consists of the goods and services account (the trade balance), the primary account, the secondary income account, the capital account, and the financial account. It is useful for policymakers, economists, and analysts as it provides insights into a country's financial stability, the effectiveness of its fiscal and monetary policies, and its economic relationship with other countries. Understanding the balance of payments can help in making informed decisions regarding economic policies and strategies for sustainable growth. More specifically, this indicator is related to the transfer income account of the balance of payments, which shows redistribution of income, that is, when resources for current purposes are provided by one party without anything of economic value being supplied as a direct return to that party. Examples include personal transfers and current international assistance. This information is valuable for (i) economic analysis: It helps economists and policymakers understand the flow of resources that do not arise from trade in goods and services or from financial investment activities; (ii) policy formulation: Governments can use this data to formulate fiscal and monetary policies, especially in countries where remittances form a significant part of the economy; (iii) measuring the social impact of emigration, as remittances can be a major source of income for households in developing countries; (iv) providing insights into the scale and impact of international aid and can help in assessing the effectiveness of aid policies.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Offices (NSOs);\nNational Accounts data files, Central Banks;\nStaff estimates, World Bank (WB). Indicator NY.TRF.NCTR.KN (https://data.worldbank.org/indicator/NY.TRF.NCTR.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.TRF.NCTR.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "ny_ttf_gnfs_kn": { "title": "Terms of trade adjustment (constant LCU)", "description_from_producer": "The terms of trade adjustment is equal to the capacity to import (current price value of exports of goods and services deflated by the import price index) less exports of goods and services in constant prices. This indicator is expressed in constant prices, meaning the series has been adjusted to account for price changes over time. The reference year for this adjustment varies by country. This series is expressed in local currency units.\n\n### Statistical concept and methodology:\nMethodology: National accounts are compiled in accordance with international standards: System of National Accounts, 2008 or 1993 versions. Specific information on how countries compile their national accounts can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual elements of the SNA (System of National Accounts) measure what takes place in the economy, between which agents, and for what purpose. At the heart of the SNA is the production of goods and services. These may be used for consumption in the period to which the accounts relate or may be accumulated for use in a later period. In simple terms, the amount of value added generated by production represents GDP. The income corresponding to GDP is distributed to the various agents or groups of agents as income and it is the process of distributing and redistributing income that allows one agent to consume the goods and services produced by another agent or to acquire goods and services for later consumption. The way in which the SNA captures this pattern of economic flows is to identify the activities concerned by recognizing the institutional units in the economy and by specifying the structure of accounts capturing the transactions relevant to one stage or another of the process by which goods and services are produced and ultimately consumed.\n\n### Development relevance:\nThis indicator is related to the national accounts, which are critical for understanding and managing a country's economy. They provide a framework for the analysis of economic performance. National accounts are the basis for estimating the Gross Domestic Product (GDP) and Gross National Income (GNI), which are the most widely used indicator of economic performance. They are essential for government policymakers, providing the data needed to design and assess fiscal and monetary policies; and are also used by businesses and investors to assess the economic climate and make investment decisions. NAS enable comparison between economies, which is crucial for international trade, investment decisions, and economic competitiveness. More specifically, this indicator is related to national accounts aggregates. Gross Domestic Product (GDP), Gross National Income (GNI), and other aggregates provide a snapshot of the size and health of an economy by measuring the total economic activity within a country. They can thus be used by policymakers to design and implement economic policies, as they reflect the overall economic performance and can indicate the need for intervention in certain areas. Aggregates also allow for comparisons between different economies, which can be useful for trade negotiations, investment decisions, and economic benchmarking. By examining aggregates over time, economists and analysts can identify trends, cycles, and potential areas of concern within an economy, and investors can use national accounts aggregates to assess the potential risks and returns of investing in a particular country. Overall, national accounts aggregates are fundamental tools for economic analysis, policy formulation, and decision-making at both the national and international levels.", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.TTF.GNFS.KN (https://data.worldbank.org/indicator/NY.TTF.GNFS.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.TTF.GNFS.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant LCU", "display": { "unit": "constant LCU" } }, "pa_nus_atls": { "title": "DEC alternative conversion factor (LCU per US$)", "description_from_producer": "The DEC alternative conversion factor is the underlying annual exchange rate (the price of one country\u2019s currency in relation to another country's currency) used for the World Bank Atlas method. As a rule, it is the official exchange rate reported in the IMF's International Financial Statistics. Exceptions arise where further refinements are made by World Bank staff. It is expressed in local currency units per U.S. dollar.\n\n### Statistical concept and methodology:\nMethodology: The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In certain countries, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. Consequently, an alternative conversion factor is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate effectively applied to domestic transactions of foreign currencies and traded products. This applies to only a small number of countries, as shown in the country-level metadata. An alternative conversion factor is also used when the period covered by national accounts differs from the calendar year and the alternative conversion factor will then cover the same period. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.\nStatistical concept(s): The World Bank systematically assesses the appropriateness of official exchange rates as conversion factors. In certain countries, multiple or dual exchange rate activity exists and must be accounted for appropriately in underlying statistics. Doing so better reflects economic reality and leads to more accurate cross-country comparisons and country classifications by income level. Consequently, an alternative conversion factor is used when the official exchange rate is judged to diverge by an exceptionally large margin from the rate effectively applied to domestic transactions of foreign currencies and traded products. This applies to only a small number of countries, as shown in the country-level metadata. An alternative conversion factor is also used when the period covered by national accounts differs from the calendar year and the alternative conversion factor will then cover the same period. Alternative conversion factors are used in the Atlas methodology and elsewhere in World Development Indicators as single-year conversion factors.\n\n### Development relevance:\nIn a market-based economy, household, producer, and government choices about resource allocation are influenced by relative prices, including the real exchange rate, real wages, real interest rates, and other prices in the economy. Relative prices also largely reflect these agents' choices. Thus relative prices convey vital information about the interaction of economic agents in an economy and with the rest of the world.\n\n### Other notes:\nIn the WDI database, the DEC alternative conversion factor is used to convert data in local currency units (LCU) into U.S. dollars.", "origins": [ { "producer": "IMF International Financial Statistics and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF);\nStaff estimates, World Bank (WB). Indicator PA.NUS.ATLS (https://data.worldbank.org/indicator/PA.NUS.ATLS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/PA.NUS.ATLS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "LCU per US$", "short_unit": "LCU", "display": { "shortUnit": "LCU", "unit": "LCU per US$" } }, "pa_nus_fcrf": { "title": "Official exchange rate (LCU per US$, period average)", "description_from_producer": "Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. This indicator represents the ratio of Local Currency Units relative to United States dollars.This indicator is derived as an average over the reference period.\n\n### Statistical concept and methodology:\nMethodology: The exchange rate is the price of one currency divided by another. Official exchange rates and exchange rate arrangements are established by governments. Other exchange rates recognized by governments include market rates, which are determined largely by legal market forces, and for countries with multiple exchange arrangements, principal rates, secondary rates, and tertiary rates. Annual average exchange rates are derived as the simple average of daily exchange rates for a specified calendar year\nStatistical concept(s): The exchange rate is the price of one currency in terms of another. Official exchange rates and exchange rate arrangements are established by governments. Other exchange rates recognized by governments include market rates, which are determined largely by legal market forces, and for countries with multiple exchange arrangements, principal rates, secondary rates, and tertiary rates.\n\n### Development relevance:\nIn a market-based economy, household, producer, and government choices about resource allocation are influenced by relative prices, including the real exchange rate, real wages, real interest rates, and other prices in the economy. Relative prices also largely reflect these agents' choices. Thus relative prices convey vital information about the interaction of economic agents in an economy and with the rest of the world.\n\n### Limitations and exceptions:\nOfficial or market exchange rates are often used to convert economic statistics in local currencies to a common currency in order to make comparisons across countries. Since market rates reflect at best the relative prices of tradable goods, the volume of goods and services that a U.S. dollar buys in the United States may not correspond to what a U.S. dollar converted to another country's currency at the official exchange rate would buy in that country, particularly when nontradable goods and services account for a significant share of a country's output. An alternative exchange rate - the purchasing power parity (PPP) conversion factor - is preferred because it reflects differences in price levels for both tradable and nontradable goods and services and therefore provides a more meaningful comparison of real output.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator PA.NUS.FCRF (https://data.worldbank.org/indicator/PA.NUS.FCRF). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/PA.NUS.FCRF", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "LCU per US$, period average", "short_unit": "LCU", "display": { "shortUnit": "LCU", "unit": "LCU per US$, period average" } }, "pa_nus_gdp_pli": { "title": "Price level index (GDP)", "description_short": "Ratio of Purchasing Power Parity (PPP) conversion factor to market exchange rate, expressed as an index with the United States equal to 100.", "description_from_producer": "The price level index (PLI) is the ratio of a purchasing power parity (PPP) conversion factor to the corresponding market exchange rate between two countries, expressed relative to a base country that is set equal to 100. For this series the base country is the United States. It provides a measure of the differences in price level between the country and the United States by indicating the number of units of the common currency (US dollars) needed to buy the same volume of the aggregation level in each country. At the level of GDP, the price level ratio provides a measure of the differences in the general price levels of countries.\n\n### Aggregation method:\nNo aggregation provided for this indicator in WDI.\n\n### Statistical concept and methodology:\nMethodology: For more information on market exchange rate, please refer to the metadata for \"DEC alternative conversion factor (LCU per US$)\" [PA.NUS.ATLS].\nFor the concept and methodology of PPP, please refer to the International Comparison Program (ICP)\u2019s website (https://www.worldbank.org/en/programs/icp).\nStatistical concept(s): A measure of price level in a given country for a basket of goods and services is the ratio of the PPP for a particular basket to the market exchange rate for the currency. Thus the price level\nindex for country j with respect to a commodity group is given by\nPLIj = (PPPj / XRj)*100\nwhere XRj is the exchange rate of the currency of country j.\n\n### Development relevance:\nThe price level index (PLI) provides a comparison of price levels across countries. If a country\u2019s PLI is lower than that of another country, then its items or expenditure aggregates are less expensive than those in the other country. Conversely, if a country\u2019s PLI is higher than that of another country, then its items or expenditure aggregates are more expensive than those in the other country.\nPurchasing power parities (PPPs), PLIs, and PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n- Recommended uses of price level ratios include: to make spatial comparisons of price levels.\n- Recommended uses of price level ratios with limitations include: to analyze changes over time in relative prices; to analyze price convergence; and to make spatial comparisons of the cost of living.\n\n### Limitations and exceptions:\nGlobal PPP estimates underlying this indicator are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\nPrice level index are not recommended to use as a precise measure to establish strict rankings of countries.", "description_key": "- It shows whether a country\u2019s currency has more or less purchasing power than its exchange rate implies, compared to the United States.\n- A value below 100 means local goods and services are cheaper than in the US, while a value above 100 suggests local prices are higher.\n- These comparisons are based on PPP conversion rates (also called PPP conversion factors or PPP rates). PPP rates estimate how many units of a country\u2019s local currency are needed to buy (roughly) the same basket of goods and services in that country that one US dollar would buy in the United States. For this indicator (at the level of [GDP](#dod:gdp)), the comparison is made using a broad, economy-wide basket of goods and services.\n- PPP rates are calculated by the [International Comparison Program (ICP)](https://www.worldbank.org/en/programs/icp), which uses the United States as a benchmark country.\n- This data is expressed in [international-$](#dod:int_dollar_abbreviation) at 2021 prices.", "origins": [ { "producer": "World Development Indicators - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Development Indicators, World Bank (WB), uri: https://databank.worldbank.org/source/world-development-indicators, publisher: World Development Indicators. Indicator PA.NUS.GDP.PLI (https://data.worldbank.org/indicator/PA.NUS.GDP.PLI). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/PA.NUS.GDP.PLI", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "", "short_unit": "", "display": { "name": "GDP price levels relative to the US", "numDecimalPlaces": 1, "tolerance": 5 }, "presentation": { "attribution_short": "World Bank", "topic_tags": [ "Economic Growth" ] } }, "pa_nus_ppp": { "title": "PPP conversion factor, GDP (LCU per international $)", "description_from_producer": "The purchasing power parity (PPP) conversion factor is a currency conversion factor and a spatial price deflator. PPPs convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of gross domestic product (GDP) and its expenditure components. This conversion factor is for the level of GDP and the base currency is the US dollar.\n\n### Aggregation method:\nNo aggregation provided for this indicator.\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world\u2019s countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The recent 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model.\n\nICP estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for years earlier years, and 2021 PPPs for later years. Description of WDI extrapolation approach is available here: https://datahelpdesk.worldbank.org/knowledgebase/articles/665452-how-do-you-extrapolate-the-ppp-conversion-factors\n\nFor the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations\u2019 databases. For Eurostat-OECD PPP Programme, please refer to the following websites.\n(http://www.oecd.org/sdd/prices-ppp/)\n(https://ec.europa.eu/eurostat/web/purchasing-power-parities/overview)\n\nFor more information on the ICP and PPPs, please refer to the ICP website at https://www.worldbank.org/en/programs/icp.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates.\n\nPPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. See https://www.worldbank.org/en/programs/icp/methodology.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nPPPs, PLIs, and the PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n- Recommended uses of PPPs include: to make spatial comparisons of GDP and its expenditure components; to make spatial comparisons of price levels; and to group countries by their per capita volume indexes and price level indexes.\n- Recommended uses of PPPs with limitations include: to analyze changes over time in relative GDP per capita and relative prices; to analyze price convergence; to make spatial comparisons of the cost of living; and to use PPPs calculated for GDP and its expenditure components as deflators for other values.\n\n### Limitations and exceptions:\nGlobal PPP estimates provided by ICP are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\n\nPPPs are not recommended to be used as: a precise measure to establish strict rankings of countries; a means of constructing national growth rates; a measure to generate output and productivity comparisons by industry; an indicator of the undervaluation or overvaluation of currencies; and as an equilibrium exchange rate.", "origins": [ { "producer": "ICP, Eurostat PPP Programme, and OECD PPP Programme, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, publisher: International Comparison Program, type: International statistical program, date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat, type: International statistical program;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-https://data-explorer.oecd.org/, publisher: OECD. Indicator PA.NUS.PPP (https://data.worldbank.org/indicator/PA.NUS.PPP). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/PA.NUS.PPP", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "LCU per international $", "short_unit": "LCU", "display": { "shortUnit": "LCU", "unit": "LCU per international $" } }, "pa_nus_prvt_pli": { "title": "Price level index (Households and NPISHs Final consumption expenditure)", "description_from_producer": "The price level index (PLI) is the ratio of a purchasing power parity (PPP) conversion factor to the corresponding market exchange rate between two countries, expressed relative to a base country that is set equal to 100. For this series the base country is the United States. It provides a measure of the differences in price level between the country and the United States by indicating the number of units of the common currency (US dollars) needed to buy the same volume of the aggregation level in each country. This indicator provides a measure of the differences in the price levels of countries at the level of Households and NPISHs final consumption expenditure.\n\n### Aggregation method:\nNo aggregation provided for this indicator in WDI.\n\n### Statistical concept and methodology:\nMethodology: For more information on market exchange rate, please refer to the metadata for \"DEC alternative conversion factor (LCU per US$)\" [PA.NUS.ATLS].\nFor the concept and methodology of PPP, please refer to the International Comparison Program (ICP)\u2019s website (https://www.worldbank.org/en/programs/icp).\nStatistical concept(s): A measure of price level in a given country for a basket of goods and services is the ratio of the PPP for a particular basket to the market exchange rate for the currency. Thus the price level\nindex for country j with respect to a commodity group is given by\nPLIj = (PPPj / XRj)*100\nwhere XRj is the exchange rate of the currency of country j.\n\n### Development relevance:\nThe price level index (PLI) provides a comparison of price levels across countries. If a country\u2019s PLI is lower than that of another country, then its items or expenditure aggregates are less expensive than those in the other country. Conversely, if a country\u2019s PLI is higher than that of another country, then its items or expenditure aggregates are more expensive than those in the other country.\nPurchasing power parities (PPPs), PLIs, and PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n- Recommended uses of price level ratios include: to make spatial comparisons of price levels.\n- Recommended uses of price level ratios with limitations include: to analyze changes over time in relative prices; to analyze price convergence; and to make spatial comparisons of the cost of living.\n\n### Limitations and exceptions:\nGlobal PPP estimates underlying this indicator are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\nPrice level index are not recommended to use as a precise measure to establish strict rankings of countries.", "origins": [ { "producer": "World Development Indicators - World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Development Indicators, World Bank (WB), uri: https://databank.worldbank.org/source/world-development-indicators, publisher: World Development Indicators. Indicator PA.NUS.PRVT.PLI (https://data.worldbank.org/indicator/PA.NUS.PRVT.PLI). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/PA.NUS.PRVT.PLI", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "pa_nus_prvt_pp": { "title": "PPP conversion factor, households and NPISHs Final consumption expenditure (LCU per international $)", "description_from_producer": "The purchasing power parity (PPP) conversion factor is a currency conversion factor and a spatial price deflator. They convert different currencies to a common currency and, in the process of conversion, equalize their purchasing power by eliminating the differences in price levels between countries, thereby allowing volume or output comparisons of gross domestic product (GDP) and its expenditure components. This conversion factor is for households and NPISHs Final consumption expenditure and the base currency is the US dollar.\n\n### Aggregation method:\nNo aggregation provided for this indicator.\n\n### Statistical concept and methodology:\nMethodology: The International Comparison Program (ICP) estimates PPPs for the world's countries. The ICP is conducted as a global partnership of countries, multilateral agencies, and academia. The 2021 ICP comparison covered 176 countries, including 49 Eurostat-OECD countries. For countries that have not participated in ICP comparisons, the PPP are imputed based on a regression model.\n\nICP-estimated PPPs cover years from 2011 to 2021. WDI extrapolates 2011 PPPs for earlier years, and 2021 PPPs for later years. Description of WDI extrapolation approach is available here: https://datahelpdesk.worldbank.org/knowledgebase/articles/665452-how-do-you-extrapolate-the-ppp-conversion-factors\n\nFor the member countries of Eurostat-OECD PPP Programme, PPP conversion factors are periodically updated based on the organizations' databases. For Eurostat-OECD PPP Programme, please refer to the following websites.\n(http://www.oecd.org/sdd/prices-ppp/)\n(https://ec.europa.eu/eurostat/web/purchasing-power-parities/overview)\n\nFor more information on the ICP and PPPs, please refer to the ICP website at https://www.worldbank.org/en/programs/icp.\nStatistical concept(s): PPPs are primarily used to convert the national accounts data of economies, such as GDP and its expenditure components, into a common currency. In the process of conversion, they control for differences in the price levels of economies, and thus equalize purchasing power. PPP-based comparisons of economic output differ from market exchange rate-based comparisons as the latter do not distinguish between the relative price levels of different items in economies. Overall price levels are normally higher in higher-income economies than they are in lower-income economies (Balassa-Samuelson effect), mostly because of the large differences in price levels for non-traded products. If no account is taken of the larger price level differences for non-traded products when converting GDP to a common currency, the size of higher-income economies with high price levels will be overstated and the size of lower-income economies with low price levels will be understated. No distinction is made between traded products and non-traded products when market exchange rates are used to convert GDP to a common currency: the rate is the same for all products. PPP-converted GDP does not have this bias because PPPs account for the different price levels of traded products and non-traded products. Thus, PPPs are more appropriate for comparing the output of economies and the average material well-being of their inhabitants and are also less impacted by the potential volatility of market exchange rates.\n\nPPPs are calculated by the International Comparison Program (ICP) based on the prices of goods and services within an economy and national accounts expenditures. See https://www.worldbank.org/en/programs/icp/methodology.\n\n### Development relevance:\nPPPs are used to convert national accounts data from different countries, such as GDP and its expenditure components, into a common currency, while also eliminating the effect of price level differences between countries. PPPs are also used to derive price level indexes (PLIs), the ratio of a country\u2019s PPP to its market exchange rate, to directly compare price levels across countries.\nPPPs, PLIs, and the PPP-based expenditures to which they give rise are primarily used to make spatial comparisons of volume and per capita consumption or levels of GDP and its expenditure components across countries. PPP-based indicators are used for national, regional, and global policy making and analysis across the socioeconomic spectrum from poverty and inequality, to health and education, to energy and climate, through to economic growth, labor, productivity, trade, competitiveness, and infrastructure. A number of Sustainable Development Goals use PPP-based indicators to measure development progress.\n- Recommended uses of PPPs include: to make spatial comparisons of GDP and its expenditure components; to make spatial comparisons of price levels; and to group countries by their per capita volume indexes and price level indexes.\n- Recommended uses of PPPs with limitations include: to analyze changes over time in relative GDP per capita and relative prices; to analyze price convergence; to make spatial comparisons of the cost of living; and to use PPPs calculated for GDP and its expenditure components as deflators for other values.\n\n### Limitations and exceptions:\nGlobal PPP estimates provided by ICP are produced by the ICP Global Office and regional implementing agencies, based on data supplied by the national implementing agencies in the participating economies, and in accordance with the methodology recommended by the ICP Technical Advisory Group and approved by the ICP Governing Board. As such, these results are not produced by participating economies as part of their national official statistics.\n\nPPPs are not recommended to be used as: a precise measure to establish strict rankings of countries; a means of constructing national growth rates; a measure to generate output and productivity comparisons by industry; an indicator of the undervaluation or overvaluation of currencies; and as an equilibrium exchange rate.", "origins": [ { "producer": "ICP, Eurostat PPP Programme, and OECD PPP Programme, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Comparison Program (ICP), World Bank (WB), uri: https://www.worldbank.org/en/programs/icp/data, publisher: International Comparison Program, type: International statistical program, date accessed: May 30, 2024, date published: May 30, 2024;\nThe Eurostat PPP Programme, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/databrowser/explore/all/all_themes, publisher: Eurostat, type: International statistical program;\nThe OECD PPP Programme, Organisation for Economic Co-operation and Development (OECD), uri: https://data-https://data-explorer.oecd.org/, publisher: OECD. Indicator PA.NUS.PRVT.PP (https://data.worldbank.org/indicator/PA.NUS.PRVT.PP). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/PA.NUS.PRVT.PP", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "LCU per international $", "short_unit": "LCU", "display": { "shortUnit": "LCU", "unit": "LCU per international $" } }, "px_rex_reer": { "title": "Real effective exchange rate index (2010 = 100)", "description_from_producer": "Real effective exchange rate is the nominal effective exchange rate (a measure of the value of a currency against a weighted average of several foreign currencies) divided by a price deflator or index of costs. This indicator is an index series where 2010=100.\n\n### Statistical concept and methodology:\nMethodology: The real effective exchange rate is a nominal effective exchange rate index adjusted for relative movements in national price or cost indicators of the home country, selected countries, and the euro area. A nominal effective exchange rate index is the ratio (expressed on the base 2010 = 100) of an index of a currency's period-average exchange rate to a weighted geometric average of exchange rates for currencies of selected countries and the euro area. For most high-income countries weights are derived from industrial country trade in manufactured goods. Data are compiled from the nominal effective exchange rate index and a cost indicator of relative normalized unit labor costs in manufacturing. For selected other countries the nominal effective exchange rate index is based on manufactured goods and primary products trade with partner or competitor countries. For these countries the real effective exchange rate index is the nominal index adjusted for relative changes in consumer prices; an increase represents an appreciation of the local currency.\nStatistical concept(s): The real effective exchange rate is a nominal effective exchange rate index adjusted for relative movements in national price or cost indicators of the home country, selected countries, and the euro area. A nominal effective exchange rate index is the ratio (expressed on the base 2010 = 100) of an index of a currency's period-average exchange rate to a weighted geometric average of exchange rates for currencies of selected countries and the euro area. For most high-income countries weights are derived from industrial country trade in manufactured goods. Data are compiled from the nominal effective exchange rate index and a cost indicator of relative normalized unit labor costs in manufacturing. For selected other countries the nominal effective exchange rate index is based on manufactured goods and primary products trade with partner or competitor countries. For these countries the real effective exchange rate index is the nominal index adjusted for relative changes in consumer prices; an increase represents an appreciation of the local currency.\n\n### Development relevance:\nIn a market-based economy, household, producer, and government choices about resource allocation are influenced by relative prices, including the real exchange rate, real wages, real interest rates, and other prices in the economy. Relative prices also largely reflect these agents' choices. Thus relative prices convey vital information about the interaction of economic agents in an economy and with the rest of the world.\n\n### Limitations and exceptions:\nBecause of conceptual and data limitations, changes in real effective exchange rates should be interpreted with caution.", "origins": [ { "producer": "International Monetary Fund (IMF) International Financial Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Financial Statistics database, International Monetary Fund (IMF). Indicator PX.REX.REER (https://data.worldbank.org/indicator/PX.REX.REER). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/PX.REX.REER", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2010 = 100", "display": { "unit": "2010 = 100" } }, "se_adt_1524_lt_fe_zs": { "title": "Literacy rate, youth female (% of females ages 15-24)", "description_from_producer": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated by the number of literate youths divided by the total number of youths, excluding youths with unknown literacy status.\n\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n### Development relevance:\nLiteracy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role.\n\n### Limitations and exceptions:\nIn practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. And there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.ADT.1524.LT.FE.ZS (https://data.worldbank.org/indicator/SE.ADT.1524.LT.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.ADT.1524.LT.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of females ages 15-24", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of females ages 15-24" } }, "se_adt_1524_lt_fm_zs": { "title": "Literacy rate, youth (ages 15-24), gender parity index (GPI)", "description_from_producer": "Gender parity index for youth literacy rate is the ratio of females to males ages 15-24 who can both read and write with understanding a short simple statement about their everyday life.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing female youth literacy rate by male youth literacy rate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiteracy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n### Development relevance:\nLiteracy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEliminating gender disparities in education would help increase the status and capabilities of women. Literate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role.\n\n### Limitations and exceptions:\nIn practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. And there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.ADT.1524.LT.FM.ZS (https://data.worldbank.org/indicator/SE.ADT.1524.LT.FM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.ADT.1524.LT.FM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "GPI", "short_unit": "GPI", "display": { "shortUnit": "GPI", "unit": "GPI" } }, "se_adt_1524_lt_ma_zs": { "title": "Literacy rate, youth male (% of males ages 15-24)", "description_from_producer": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated by the number of literate youths divided by the total number of youths, excluding youths with unknown literacy status.\n\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n### Development relevance:\nLiteracy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role.\n\n### Limitations and exceptions:\nIn practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. And there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.ADT.1524.LT.MA.ZS (https://data.worldbank.org/indicator/SE.ADT.1524.LT.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.ADT.1524.LT.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of males ages 15-24", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of males ages 15-24" } }, "se_adt_1524_lt_zs": { "title": "Literacy rate, youth total (% of people ages 15-24)", "description_from_producer": "Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data is calculated by dividing the number of literate persons by the total number of persons in the same age group, excluding persons with unknown literacy status.\n\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n### Development relevance:\nLiteracy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women imply that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role.\n\n### Limitations and exceptions:\nIn practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. And there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.ADT.1524.LT.ZS (https://data.worldbank.org/indicator/SE.ADT.1524.LT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.ADT.1524.LT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of people ages 15-24", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of people ages 15-24" } }, "se_adt_litr_fe_zs": { "title": "Literacy rate, adult female (% of females ages 15 and above)", "description_from_producer": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status.\n\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n### Development relevance:\nLiteracy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role.\n\n### Limitations and exceptions:\nIn practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. Ant there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.ADT.LITR.FE.ZS (https://data.worldbank.org/indicator/SE.ADT.LITR.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.ADT.LITR.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of females ages 15 and above", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of females ages 15 and above" } }, "se_adt_litr_ma_zs": { "title": "Literacy rate, adult male (% of males ages 15 and above)", "description_from_producer": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status.\n\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n### Development relevance:\nLiteracy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role.\n\n### Limitations and exceptions:\nIn practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. And there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.ADT.LITR.MA.ZS (https://data.worldbank.org/indicator/SE.ADT.LITR.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.ADT.LITR.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of males ages 15 and above", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of males ages 15 and above" } }, "se_adt_litr_zs": { "title": "Literacy rate, adult total (% of people ages 15 and above)", "description_from_producer": "Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated by the number of literate adults divided by the total number of adults, excluding adults with unknown literacy status.\n\n\n\n\n\nData on literacy are compiled by the UNESCO Institute for Statistics based on national censuses and household surveys and, for countries without recent literacy data, using the Global Age-Specific Literacy Projection Model (GALP). For detailed information, see www.uis.unesco.org.\nStatistical concept(s): Literacy statistics for most countries cover the population ages 15 and older, but some include younger ages or are confined to age ranges that tend to inflate literacy rates. The youth literacy rate for ages 15-24 reflects recent progress in education. It measures the accumulated outcomes of primary education over the previous 10 years or so by indicating the proportion of the population who have passed through the primary education system and acquired basic literacy and numeracy skills. Generally, literacy also encompasses numeracy, the ability to make simple arithmetic calculations.\n\n### Development relevance:\nLiteracy rate is an outcome indicator to evaluate educational attainment. This data can predict the quality of future labor force and can be used in ensuring policies for life skills for men and women.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nIt can be also used as a proxy instrument to see the effectiveness of education system; a high literacy rate suggests the capacity of an education system to provide a large population with opportunities to acquire literacy skills. The accumulated achievement of education is fundamental for further intellectual growth and social and economic development, although it doesn't necessarily ensure the quality of education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLiterate women implies that they can seek and use information for the betterment of the health, nutrition and education of their household members. Literate women are also empowered to play a meaningful role.\n\n### Limitations and exceptions:\nIn practice, literacy is difficult to measure. Estimating literacy rates requires census or survey measurements under controlled conditions. Many countries report the number of literate people from self-reported data. Some use educational attainment data as a proxy but apply different lengths of school attendance or levels of completion. And there is a trend among recent national and international surveys toward using a direct reading test of literacy skills. Because definitions and methods of data collection differ across countries, data should be used cautiously.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Excel (bulk file), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.ADT.LITR.ZS (https://data.worldbank.org/indicator/SE.ADT.LITR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.ADT.LITR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of people ages 15 and above", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of people ages 15 and above" } }, "se_com_durs": { "title": "Compulsory education, duration (years)", "description_from_producer": "Duration of compulsory education is the number of years that children are legally obliged to attend school.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Compulsory education is defined as educational programs that children and young people are legally obliged to attend, usually defined in terms of a number of grades or an age range, or both (UNESCO, https://unesdoc.unesco.org/ark:/48223/pf0000141639).\n\n### Development relevance:\nCompulsory education is recognized as a fundamental human right. The Universal Declaration of Human Rights (https://www.un.org/en/about-us/universal-declaration-of-human-rights/) advocates for free and mandatory primary education. Additionally, the Convention on the Rights of the Child (https://www.ohchr.org/en/instruments-mechanisms/instruments/convention-rights-child ) expands on this by mandating that states should strive to make secondary education available and accessible to everyone.\n\n### Limitations and exceptions:\nThe presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.COM.DURS (https://data.worldbank.org/indicator/SE.COM.DURS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.COM.DURS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "years", "short_unit": "years", "display": { "shortUnit": "years", "unit": "years" } }, "se_enr_prim_fm_zs": { "title": "School enrollment, primary (gross), gender parity index (GPI)", "description_from_producer": "Gender parity index for gross enrollment ratio in primary education is the ratio of girls to boys enrolled at primary level in public and private schools.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing female gross enrollment ratio in primary education by male gross enrollment ratio in primary education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.ENR.PRIM.FM.ZS (https://data.worldbank.org/indicator/SE.ENR.PRIM.FM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.ENR.PRIM.FM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "GPI", "short_unit": "GPI", "display": { "shortUnit": "GPI", "unit": "GPI" } }, "se_enr_prsc_fm_zs": { "title": "School enrollment, primary and secondary (gross), gender parity index (GPI)", "description_from_producer": "Gender parity index for gross enrollment ratio in primary and secondary education is the ratio of girls to boys enrolled at primary and secondary levels in public and private schools.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing female gross enrollment ratio in primary and secondary education by male gross enrollment ratio in primary and secondary education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nThe Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.ENR.PRSC.FM.ZS (https://data.worldbank.org/indicator/SE.ENR.PRSC.FM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.ENR.PRSC.FM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "GPI", "short_unit": "GPI", "display": { "shortUnit": "GPI", "unit": "GPI" } }, "se_enr_seco_fm_zs": { "title": "School enrollment, secondary (gross), gender parity index (GPI)", "description_from_producer": "Gender parity index for gross enrollment ratio in secondary education is the ratio of girls to boys enrolled at secondary level in public and private schools.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing female gross enrollment ratio in secondary education by male gross enrollment ratio in secondary education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around. Eliminating gender disparities in education would help increase the status and capabilities of women.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.ENR.SECO.FM.ZS (https://data.worldbank.org/indicator/SE.ENR.SECO.FM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.ENR.SECO.FM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "GPI", "short_unit": "GPI", "display": { "shortUnit": "GPI", "unit": "GPI" } }, "se_enr_tert_fm_zs": { "title": "School enrollment, tertiary (gross), gender parity index (GPI)", "description_from_producer": "Gender parity index for gross enrollment ratio in tertiary education is the ratio of women to men enrolled at tertiary level in public and private schools.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing female gross enrollment ratio in tertiary education by male gross enrollment ratio in tertiary education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The Gender Parity Index (GPI) indicates parity between girls and boys. A GPI of less than 1 suggests girls are more disadvantaged than boys in learning opportunities and a GPI of greater than 1 suggests the other way around.\n\n### Development relevance:\nEducation is a basic human entitlement, and it is crucial that both girls and boys are afforded equal chances to learn. The Sustainable Development Goal (SDG) Target 4.5 focuses on eliminating gender disparities in education and ensuring equal access to all levels of education for both girls and boys. This target is part of a broader commitment to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, as outlined in SDG 4. The pursuit of gender equality in education is not only a matter of fairness and equity but also has significant implications for economic development, empowerment, and the well-being of communities and nations.\n\n### Limitations and exceptions:\nThe gross enrolment ratio is a general measure of participation in tertiary education. However, it does not account for variations in the duration of programs between countries or across different levels of education and fields of study. While it is somewhat standardized by measuring it relative to a 5-year age group for all countries, it may still underestimate participation, particularly in countries with underdeveloped tertiary education systems or where offerings are limited to initial tertiary programs, which are typically shorter than 5 years in duration.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.ENR.TERT.FM.ZS (https://data.worldbank.org/indicator/SE.ENR.TERT.FM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.ENR.TERT.FM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "GPI", "short_unit": "GPI", "display": { "shortUnit": "GPI", "unit": "GPI" } }, "se_lpv_prim": { "title": "Learning poverty: Share of Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)", "description_from_producer": "The share of 10-year-olds who cannot read and understand a short passage of age-appropriate material\u2014in other words, those who are below the \u201cminimum proficiency\u201d threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This \u201cunion approach\u201d to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven\u2019t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived).\n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty.\n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define \u201ccurrent.\u201d We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10\u201314 years old. To obtain a global estimate, we weight the regional aggregations by the 10\u201314-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading\n\n### Development relevance:\nEnsuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school.\n\n\nThe LP indicator illustrates progress toward SDG 4\u2019s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank\u2019s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school\u2014generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries\u2019 Human Capital Index scores.\n\n### Limitations and exceptions:\nThe construct of \"all children reading by age 10\" is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7.\n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or an international or regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML's efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS). Indicator SE.LPV.PRIM (https://data.worldbank.org/indicator/SE.LPV.PRIM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.LPV.PRIM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_lpv_prim_fe": { "title": "Learning poverty: Share of Female Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)", "description_from_producer": "The share of female 10-year-olds who cannot read and understand a short passage of age-appropriate material\u2014in other words, those who are below the \u201cminimum proficiency\u201d threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This \u201cunion approach\u201d to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven\u2019t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived).\n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty.\n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define \u201ccurrent.\u201d We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10\u201314 years old. To obtain a global estimate, we weight the regional aggregations by the 10\u201314-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading\n\n### Development relevance:\nEnsuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school.\n\n\nThe LP indicator illustrates progress toward SDG 4\u2019s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank\u2019s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school\u2014generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries\u2019 Human Capital Index scores.\n\n### Limitations and exceptions:\nThe construct of \u201call children reading by age 10\u201d is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7.\n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or a international of regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML\u2019s efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS). Indicator SE.LPV.PRIM.FE (https://data.worldbank.org/indicator/SE.LPV.PRIM.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.LPV.PRIM.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_lpv_prim_ld": { "title": "Pupils below minimum reading proficiency at end of primary (%). Low GAML threshold", "description_from_producer": "The share of pupils at the end of primary schooling who are below the minimum proficiency level (MPL) for reading or learning deprived. The MPL in reading at the end of primary is defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. It is \u201cStudents independently and fluently read simple, short narrative and expository texts. They locate explicitly-stated information. They interpret and give some explanations about the key ideas in these texts. They provide simple, personal opinions or judgements about the information, events and characters in a text.\u201d (UIS and GAML 2019). In other words, a child \u201cattaining\u201d minimum proficiency has the ability to read and understand a short passage of age-appropriate material, whether a simple story or non-fiction narrative of a few paragraphs. In addition to this nutshell statement, the GAML has also proposed a common terminology to describe classifications in the context of the MPL. This is a critical first step toward linking cross-national and national learning assessments with a common benchmark.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Learning assessments used to calculate Learning Poverty have a minimum proficiency level (MPL) benchmarked by Global Alliance to Monitor Learning (GAML) under the leadership of the UNESCO Institute of Statistics (UIS), which occurred within the reporting window. To operationalize this concept, the current SDG monitoring process is followed by defining \u201cproficiency\u201d as reaching at least the Low International Benchmark on the international PIRLS literacy assessment.\n\n\nPIRLS is the major global primary-age assessment focused on reading, and if all countries participated in it, the task of constructing global estimates of minimum proficiency would be trivial, as it would require aggregating results from a single cross-national assessment. However, most countries participating in PIRLS are high-income, and only a small minority of low- and middle-income countries participate in the assessment. One of the main contributions of the GAML process is that it has overcome this data gap by benchmarking several major cross-national assessments\u2014and increasingly national learning assessments as well\u2014against the standard.\n\n\nThe MPL for each learning assessment is used to calculate the reading proficiency rate for that country, which is the share of students scoring at or above the minimum proficiency level, and conversely to calculate the learning deprivation.\n\n\nThe Proficiency and Grade Levels used for each assessment is as follows: PIRLS (grade 4) - Level 2 (Low international benchmark, 400 points); TIMSS (grade 4) - Level 2 (Low international benchmark, 400 points); LLECE (SERCE, grade 6) - Level 3 (513.66 points); PASEC (grades 5 and 6) - Level 4 (595.1 points); SEA-PLM (grade 5) - Level 6 and above; National Learning Assessment (grade 4, 5 and 6) - Varies by country.\n\n\nWhen a given country had administered multiple types of learning assessments, a hierarchy is applied in the order listed below to ensure best comparability across countries: International or Regional Learning Assessment for Reading (PIRLS, LLECE, PASEC, SEA-PLM) > TIMSS Science > Statistical or Pairwise Linking Exercises > AMPL-bs, Policy Linked National Learning Assessments or Policy Linked Service Delivery Indicators (SDIs) > Non-Policy Linked NLAs (Interim Reporting).\n\n\nNote that as the GAML and joint coalitions continue their efforts to improve learning data coverage, the hierarchy may be revised.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading\n\n### Development relevance:\nEnsuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school.\n\n\nThe LP indicator illustrates progress toward SDG 4\u2019s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank\u2019s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school\u2014generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries\u2019 Human Capital Index scores.\n\n### Limitations and exceptions:\nThe process of equating proficiency levels on different assessments to the GAML definition is not straightforward. Even the long-running regional assessment initiatives like PASEC (West and Central Africa) and LLECE (Latin America and the Caribbean) use different definitions and a different number of levels than other assessments like PIRLS, and those might not even be the same over time. Their test development methodologies and test administration procedures also vary. Moreover, because not all countries participate in global or regional assessments, for some major countries we rely on their interim reporting using their national assessments; equating these assessments is even more challenging. UIS and the World Bank have mapped how proficiency levels between assessment can equate to one another, but they are not strictly comparable.\n\nAmong the differences across assessments, one important point concerns the age at which children are tested. The reference age for our exercise is age 10. However, all learning assessments used in this analysis are sampled based on specific grades rather than age. PIRLS and TIMSS are administered in Grade 4, meaning that the average student assessed is indeed 10 years old, but this is not the case for the regional assessments. PASEC and LLECE are administered in Grade 6, so the average age in those assessments is 12.8 and 12.4, respectively. National assessments are administered at different grades, so to incorporate those assessments, we chose for each country the grade between 4 and 6 (inclusive) for which relevant and reliable data were available. This is consistent with the SDG monitoring by UIS and GAML, which lists \"End of Primary (or Grades 4 to 6)\" as the relevant age category for the end-of-primary students (SDG 4.1.1b).\n\nIn some cases, National Learning Assessments that have not been policy linked are used for learning poverty, if country teams and experts determine that an assessment is of sufficient quality or has undertaken steps to align their assessments with the Global Proficiency Framework. They will often be reported as interim learning poverty indicators, as they are not fully aligned with SDG 4.1.1b.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS). Indicator SE.LPV.PRIM.LD (https://data.worldbank.org/indicator/SE.LPV.PRIM.LD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.LPV.PRIM.LD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_lpv_prim_ld_fe": { "title": "Female pupils below minimum reading proficiency at end of primary (%). Low GAML threshold", "description_from_producer": "The share of female pupils at the end of primary schooling who are below the minimum proficiency level (MPL) for reading or learning deprived. The MPL in reading at the end of primary is defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. It is \u201cStudents independently and fluently read simple, short narrative and expository texts. They locate explicitly-stated information. They interpret and give some explanations about the key ideas in these texts. They provide simple, personal opinions or judgements about the information, events and characters in a text.\u201d (UIS and GAML 2019). In other words, a child \u201cattaining\u201d minimum proficiency has the ability to read and understand a short passage of age-appropriate material, whether a simple story or non-fiction narrative of a few paragraphs. In addition to this nutshell statement, the GAML has also proposed a common terminology to describe classifications in the context of the MPL. This is a critical first step toward linking cross-national and national learning assessments with a common benchmark.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Learning assessments used to calculate Learning Poverty have a minimum proficiency level (MPL) benchmarked by Global Alliance to Monitor Learning (GAML) under the leadership of the UNESCO Institute of Statistics (UIS), which occurred within the reporting window. To operationalize this concept, the current SDG monitoring process is followed by defining \u201cproficiency\u201d as reaching at least the Low International Benchmark on the international PIRLS literacy assessment.\n\n\nPIRLS is the major global primary-age assessment focused on reading, and if all countries participated in it, the task of constructing global estimates of minimum proficiency would be trivial, as it would require aggregating results from a single cross-national assessment. However, most countries participating in PIRLS are high-income, and only a small minority of low- and middle-income countries participate in the assessment. One of the main contributions of the GAML process is that it has overcome this data gap by benchmarking several major cross-national assessments\u2014and increasingly national learning assessments as well\u2014against the standard.\n\n\nThe MPL for each learning assessment is used to calculate the reading proficiency rate for that country, which is the share of students scoring at or above the minimum proficiency level, and conversely to calculate the learning deprivation.\n\n\nThe Proficiency and Grade Levels used for each assessment is as follows: PIRLS (grade 4) - Level 2 (Low international benchmark, 400 points); TIMSS (grade 4) - Level 2 (Low international benchmark, 400 points); LLECE (SERCE, grade 6) - Level 3 (513.66 points); PASEC (grades 5 and 6) - Level 4 (595.1 points); SEA-PLM (grade 5) - Level 6 and above; National Learning Assessment (grade 4, 5 and 6) - Varies by country.\n\n\nWhen a given country had administered multiple types of learning assessments, a hierarchy is applied in the order listed below to ensure best comparability across countries: International or Regional Learning Assessment for Reading (PIRLS, LLECE, PASEC, SEA-PLM) > TIMSS Science > Statistical or Pairwise Linking Exercises > AMPL-bs, Policy Linked National Learning Assessments or Policy Linked Service Delivery Indicators (SDIs) > Non-Policy Linked NLAs (Interim Reporting).\n\n\nNote that as the GAML and joint coalitions continue their efforts to improve learning data coverage, the hierarchy may be revised.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading\n\n### Development relevance:\nEnsuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school.\n\n\nThe LP indicator illustrates progress toward SDG 4\u2019s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank\u2019s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school\u2014generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries\u2019 Human Capital Index scores.\n\n### Limitations and exceptions:\nThe process of equating proficiency levels on different assessments to the GAML definition is not straightforward. Even the long-running regional assessment initiatives like PASEC (West and Central Africa) and LLECE (Latin America and the Caribbean) use different definitions and a different number of levels than other assessments like PIRLS, and those might not even be the same over time. Their test development methodologies and test administration procedures also vary. Moreover, because not all countries participate in global or regional assessments, for some major countries we rely on their interim reporting using their national assessments; equating these assessments is even more challenging. UIS and the World Bank have mapped how proficiency levels between assessment can equate to one another, but they are not strictly comparable.\n\nAmong the differences across assessments, one important point concerns the age at which children are tested. The reference age for our exercise is age 10. However, all learning assessments used in this analysis are sampled based on specific grades rather than age. PIRLS and TIMSS are administered in Grade 4, meaning that the average student assessed is indeed 10 years old, but this is not the case for the regional assessments. PASEC and LLECE are administered in Grade 6, so the average age in those assessments is 12.8 and 12.4, respectively. National assessments are administered at different grades, so to incorporate those assessments, we chose for each country the grade between 4 and 6 (inclusive) for which relevant and reliable data were available. This is consistent with the SDG monitoring by UIS and GAML, which lists \u201cEnd of Primary (or Grades 4 to 6)\u201d as the relevant age category for the end-of-primary students (SDG 4.1.1b).\n\nIn some cases, National Learning Assessments that have not been policy linked are used for learning poverty, if country teams and experts determine that an assessment is of sufficiently quality or has undertaken steps to align their assessments with the Global Proficiency Framework. They will often be reported as interim learning poverty indicators, as they are not fully aligned with SDG 4.1.1b.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS). Indicator SE.LPV.PRIM.LD.FE (https://data.worldbank.org/indicator/SE.LPV.PRIM.LD.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.LPV.PRIM.LD.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_lpv_prim_ld_ma": { "title": "Male pupils below minimum reading proficiency at end of primary (%). Low GAML threshold", "description_from_producer": "The share of male pupils at the end of primary schooling who are below the minimum proficiency level (MPL) for reading or learning deprived. The MPL in reading at the end of primary is defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. It is \u201cStudents independently and fluently read simple, short narrative and expository texts. They locate explicitly-stated information. They interpret and give some explanations about the key ideas in these texts. They provide simple, personal opinions or judgements about the information, events and characters in a text.\u201d (UIS and GAML 2019). In other words, a child \u201cattaining\u201d minimum proficiency has the ability to read and understand a short passage of age-appropriate material, whether a simple story or non-fiction narrative of a few paragraphs. In addition to this nutshell statement, the GAML has also proposed a common terminology to describe classifications in the context of the MPL. This is a critical first step toward linking cross-national and national learning assessments with a common benchmark.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Learning assessments used to calculate Learning Poverty have a minimum proficiency level (MPL) benchmarked by Global Alliance to Monitor Learning (GAML) under the leadership of the UNESCO Institute of Statistics (UIS), which occurred within the reporting window. To operationalize this concept, the current SDG monitoring process is followed by defining \u201cproficiency\u201d as reaching at least the Low International Benchmark on the international PIRLS literacy assessment.\n\n\nPIRLS is the major global primary-age assessment focused on reading, and if all countries participated in it, the task of constructing global estimates of minimum proficiency would be trivial, as it would require aggregating results from a single cross-national assessment. However, most countries participating in PIRLS are high-income, and only a small minority of low- and middle-income countries participate in the assessment. One of the main contributions of the GAML process is that it has overcome this data gap by benchmarking several major cross-national assessments\u2014and increasingly national learning assessments as well\u2014against the standard.\n\n\nThe MPL for each learning assessment is used to calculate the reading proficiency rate for that country, which is the share of students scoring at or above the minimum proficiency level, and conversely to calculate the learning deprivation.\n\n\nThe Proficiency and Grade Levels used for each assessment is as follows: PIRLS (grade 4) - Level 2 (Low international benchmark, 400 points); TIMSS (grade 4) - Level 2 (Low international benchmark, 400 points); LLECE (SERCE, grade 6) - Level 3 (513.66 points); PASEC (grades 5 and 6) - Level 4 (595.1 points); SEA-PLM (grade 5) - Level 6 and above; National Learning Assessment (grade 4, 5 and 6) - Varies by country.\n\n\nWhen a given country had administered multiple types of learning assessments, a hierarchy is applied in the order listed below to ensure best comparability across countries: International or Regional Learning Assessment for Reading (PIRLS, LLECE, PASEC, SEA-PLM) > TIMSS Science > Statistical or Pairwise Linking Exercises > AMPL-bs, Policy Linked National Learning Assessments or Policy Linked Service Delivery Indicators (SDIs) > Non-Policy Linked NLAs (Interim Reporting).\n\n\nNote that as the GAML and joint coalitions continue their efforts to improve learning data coverage, the hierarchy may be revised.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading\n\n### Development relevance:\nEnsuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school.\n\n\nThe LP indicator illustrates progress toward SDG 4\u2019s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank\u2019s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school\u2014generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries\u2019 Human Capital Index scores.\n\n### Limitations and exceptions:\nThe process of equating proficiency levels on different assessments to the GAML definition is not straightforward. Even the long-running regional assessment initiatives like PASEC (West and Central Africa) and LLECE (Latin America and the Caribbean) use different definitions and a different number of levels than other assessments like PIRLS, and those might not even be the same over time. Their test development methodologies and test administration procedures also vary. Moreover, because not all countries participate in global or regional assessments, for some major countries we rely on their interim reporting using their national assessments; equating these assessments is even more challenging. UIS and the World Bank have mapped how proficiency levels between assessment can equate to one another, but they are not strictly comparable.\n\nAmong the differences across assessments, one important point concerns the age at which children are tested. The reference age for our exercise is age 10. However, all learning assessments used in this analysis are sampled based on specific grades rather than age. PIRLS and TIMSS are administered in Grade 4, meaning that the average student assessed is indeed 10 years old, but this is not the case for the regional assessments. PASEC and LLECE are administered in Grade 6, so the average age in those assessments is 12.8 and 12.4, respectively. National assessments are administered at different grades, so to incorporate those assessments, we chose for each country the grade between 4 and 6 (inclusive) for which relevant and reliable data were available. This is consistent with the SDG monitoring by UIS and GAML, which lists \"End of Primary (or Grades 4 to 6)\" as the relevant age category for the end-of-primary students (SDG 4.1.1b).\n\nIn some cases, National Learning Assessments that have not been policy linked are used for learning poverty, if country teams and experts determine that an assessment is of sufficient quality or has undertaken steps to align their assessments with the Global Proficiency Framework. They will often be reported as interim learning poverty indicators, as they are not fully aligned with SDG 4.1.1b.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS). Indicator SE.LPV.PRIM.LD.MA (https://data.worldbank.org/indicator/SE.LPV.PRIM.LD.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.LPV.PRIM.LD.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_lpv_prim_ma": { "title": "Learning poverty: Share of Male Children at the End-of-Primary age below minimum reading proficiency adjusted by Out-of-School Children (%)", "description_from_producer": "The share of male 10-year-olds who cannot read and understand a short passage of age-appropriate material\u2014in other words, those who are below the \u201cminimum proficiency\u201d threshold for reading. This measure is defined as the union of two deprivations: 1) schooling deprivation and 2) learning deprivation. A child is considered schooling-deprived (SD) if he or she is of primary school age and out-of-school. The dimension of learning deprivation (LD) applies only for children in school, and identifies those pupils who are below the minimum proficiency level (MPL) for reading, as defined by the Global Alliance to Monitor Learning (GAML), measured in standard learning assessments, and reported in the context of the SDG 4.1.1b monitoring. This \u201cunion approach\u201d to measurement reflects the choice that, as presented in the SDGs, all age 10 children must be both in school and learning. The final learning poverty measure combines the two dimensions in a single indicator using the following formula: LP = SD + [(1-SD) x LD]\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The learning poverty indicator brings together schooling and learning indicators. It starts with the share of children in school who haven\u2019t achieved minimum reading proficiency (Learning Deprived) and adjusts it by the proportion of children who are out of school (Schooling Deprived).\n\n\nFormally, Learning Poverty is calculated as: [LD* (1-SD)] + [1 * SD]\n\n\nwhere LP = Learning poverty; LD = Learning deprivation or the share of children at the end of primary who read at below the minimum proficiency level, as defined by the Global Alliance to Monitor Learning (GAML) in the context of the SDG 4.1.1 monitoring; SD = Schooling deprivation or the share of primary-school-age children who are out-of-school (OOS) and in which all OOS are regarded as being below the minimum proficiency level.\n\n\nBecause out-of-school children are treated as non-proficient in reading, learning poverty will always be higher than the share of children in school who haven't achieved minimum reading proficiency. For countries with a very low schooling deprivation, the learning deprivation value will be very close to Learning Poverty.\n\n\nEstimating the current level of global and regional learning poverty requires deciding how to define \u201ccurrent.\u201d We include results of assessments within four years before or after a set anchor year. This decision is driven by data availability. International and regional large-scale learning assessments used for SDG 4.1.1b reporting are carried out only every 3 to 4 years. And even where assessments have been carried out recently, there is a lag of a couple of years before the data are available. This band is intended as a moving window. In the original 2019 release, the anchor year used was 2015 (Assessments between 2011 and 2019 are included in the learning poverty estimate). In the 2022 Global Update, the anchor year was moved to 2019 (assessments between 2015 and 2023 are included).\n\n\nAggregations for each region comprise the average learning poverty of countries with available data, weighted by their population ages 10\u201314 years old. To obtain a global estimate, we weight the regional aggregations by the 10\u201314-year-old population regardless of data availability. This is equivalent to imputing missing country data using regional values.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading\n\n### Development relevance:\nEnsuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school.\n\n\nThe LP indicator illustrates progress toward SDG 4's broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank's Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school\u2014generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries' Human Capital Index scores.\n\n### Limitations and exceptions:\nThe construct of \"all children reading by age 10\" is an ideal that embodies normative statements about both learning and access. To achieve it, not only should all children be reading proficiently after 3 full years in primary education, but they should also have entered school at age 6 or 7.\n\nBy contrast, the actual indicators used to measure learning poverty are based on grade rather than age. Since the assessments are of 4th- through 6th-graders, the children tested will have had at least 3 to 5 years in school to reach what, according to the ideal, should 10 be an age-10 minimum proficiency, or even the entire primary-school-age segment for the out-of-school indicator.\n\nDue to different assessment availability within and between countries, data comparability, both within countries over time and across countries still poses a significant challenge. The additional out of school component further limits comparability.\n\nThe learning poverty indicator is based on data covering four-fifths of children at the end of primary school. In other words, a little more than 80 percent of children in low- and middle-income countries live in a country with at least one learning assessment at the end of primary, carried out in the past 9 years. For regional and global aggregates, weighted imputations affect regions with less data coverage. The major gaps are concentrated in countries where the learning crisis is most acute. Less than half of children in Sub-Saharan Africa live in a country with a National Large-Scale Learning Assessment (NLSA) or an international or regional large-scale learning assessment (ILSA or RLSA) of adequate quality to be used for this purpose.\n\nThis extensive coverage became possible only in recent years, with the progress in measuring learning in countries and the GAML's efforts to establish comparability, which has made possible the construction of a global indicator based on harmonized proficiency levels. Future efforts by coalition organizations are also ensuring more flexible assessment options are available for expanding data availability for countries, such as the Assessment of Minimum Proficiency Levels (AMPL) and policy linking exercises led by UIS.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS). Indicator SE.LPV.PRIM.MA (https://data.worldbank.org/indicator/SE.LPV.PRIM.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.LPV.PRIM.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_lpv_prim_sd": { "title": "Primary school age children out-of-school (%)", "description_from_producer": "The share of children of primary-school age who are out of school or schooling deprived. This dimension is linked to the indicator 4.1.4 from the SDG 4 thematic framework. This element reflects the belief that all primary-age children should be learning in schools of some type, a belief that every country has enshrined in law and that is enshrined in the SDGs. In addition to fulfilling a universal right and serving as a necessary condition for sustained learning, schooling offers many benefits beyond learning. It contributes to children\u2019s health and well-being such as promoting safety, nutrition, and socialization, and facilitating parents' labor market participation and, at the macro level, schooling can help build social cohesion, democracy, and peace. All those complementary functions mean that schooling has value over and above the measured cognitive learning that it leads to, and they justify including schooling deprivation in the concept of learning poverty.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Schooling Deprivation is derived using enrollment rates computed by UIS using administrative records and follows the SDG 4.1.4 indicator. To ensure country coverage of data, we consider other enrollment definitions to use for learning poverty, if the first-best option is not available.\n\n\nWe construct an enrollment dataset from 1990 to the year of the current release, relying on multiple enrollment definitions. Our dataset is constructed from UIS (UNESCO Institute of Statistics), and other sources suggested by World Bank regional or country education specialists. Data sources are typically from administrative records (school registers or school censuses) for data on enrolment by age, and UNPD population estimates for school-age population (UIS). Enrolment by single years of age in all levels of education and the total primary-school-age population are used to compute enrollment rates.\n\n\nWe follow this hierarchy of enrollment definitions: Country specialist validated data > Adjusted Net Enrollment Rate (ANER) > Total Net Enrollment Rate (TNER) > Net enrollment rate (NER) > Gross Enrollment Rate (GER; if the gross enrollment rate is higher than 100%, it is adjusted to be 100%).\n\n\nOur preferred measure of school participation is ANER, because it accounts for some primary school aged children who might enter primary school early and advance to secondary school before they reach the official upper age limit of primary education. Adjusted net enrollment is a measure of both \u201cstock\u201d and \u201cflow\u201d and accounts for both age- and grade-based distortions, as it is the percent of primary school age children enrolled either in primary or secondary education, as opposed to gross enrollment which is the share of children of any age that are enrolled in primary school, or net enrollment which is the share of primary school age children that are enrolled in primary school. The next-best indicator is used if ANER is unavailable. In some cases, country specialists will provide a data point that reflects enrollment in the country better than UIS statistics which is used. In future Learning Poverty releases, the enrollment hierarchy may be adjusted as availability of indicator definitions change.\n\n\nThe enrollment year used is the one that best pairs with the assessment year used to compute Learning Deprivation. The year of the preferred assessment is the base. If the same enrollment year is not available, we use a step function to fill the data in with the value of the closest year. If there is data available for two years equally close to the year to fill, the older value is used. This procedure to extrapolate enrollment for missing values is required for us to pair the proficiency measure with enrollment measures from the same year, or its best proxy when enrollment is not available for the same year of the assessment.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading\n\n### Development relevance:\nEnsuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school.\n\n\nThe LP indicator illustrates progress toward SDG 4\u2019s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank\u2019s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school\u2014generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries\u2019 Human Capital Index scores.\n\n### Limitations and exceptions:\nThe enrollment for a given learning poverty release are not strictly comparable between countries, due to the differences in enrollment years and definitions, which are determined by best-match with the assessment year and data availability of enrollment indicators.\n\nThe measure will also differ from out of school estimates using household survey data, which UIS reports for SDG 4.1.4. Households survey estimates are not used for Schooling Deprivation because data is typically reported for countries in various years and with time gaps. School surveys are more feasibly collected annually, while household data collection occurs every few years and can also depend on country demand. School surveys also allow more global consistency as the same survey and source data are used across countries. However, the source used to compute the total school-age population differ in some cases where a country provides their national estimates over the default UNDP population data. However, there are potential trade-offs in precision from school surveys as responses come from school representatives rather than using microdata.\n\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, enrolment rate (any definition) is affected by different age-reference points for enrollment. The length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced. The population data further affects the reference age.\n\nDue to the limitations described, in some cases, country specialists will provide a data point that better reflects enrollment in the country.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS). Indicator SE.LPV.PRIM.SD (https://data.worldbank.org/indicator/SE.LPV.PRIM.SD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.LPV.PRIM.SD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_lpv_prim_sd_fe": { "title": "Female primary school age children out-of-school (%)", "description_from_producer": "The share of female children of primary-school age who are out of school or schooling deprived. This dimension is linked to the indicator 4.1.4 from the SDG 4 thematic framework. This element reflects the belief that all primary-age children should be learning in schools of some type, a belief that every country has enshrined in law and that is enshrined in the SDGs. In addition to fulfilling a universal right and serving as a necessary condition for sustained learning, schooling offers many benefits beyond learning. It contributes to children\u2019s health and well-being such as promoting safety, nutrition, and socialization, and facilitating parents' labor market participation and, at the macro level, schooling can help build social cohesion, democracy, and peace. All those complementary functions mean that schooling has value over and above the measured cognitive learning that it leads to, and they justify including schooling deprivation in the concept of learning poverty.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Schooling Deprivation is derived using enrollment rates computed by UIS using administrative records and follows the SDG 4.1.4 indicator. To ensure country coverage of data, we consider other enrollment definitions to use for learning poverty, if the first-best option is not available.\n\n\nWe construct an enrollment dataset from 1990 to the year of the current release, relying on multiple enrollment definitions. Our dataset is constructed from UIS (UNESCO Institute of Statistics), and other sources suggested by World Bank regional or country education specialists. Data sources are typically from administrative records (school registers or school censuses) for data on enrolment by age, and UNPD population estimates for school-age population (UIS). Enrolment by single years of age in all levels of education and the total primary-school-age population are used to compute enrollment rates.\n\n\nWe follow this hierarchy of enrollment definitions: Country specialist validated data > Adjusted Net Enrollment Rate (ANER) > Total Net Enrollment Rate (TNER) > Net enrollment rate (NER) > Gross Enrollment Rate (GER; if the gross enrollment rate is higher than 100%, it is adjusted to be 100%).\n\n\nOur preferred measure of school participation is ANER, because it accounts for some primary school aged children who might enter primary school early and advance to secondary school before they reach the official upper age limit of primary education. Adjusted net enrollment is a measure of both \u201cstock\u201d and \u201cflow\u201d and accounts for both age- and grade-based distortions, as it is the percent of primary school age children enrolled either in primary or secondary education, as opposed to gross enrollment which is the share of children of any age that are enrolled in primary school, or net enrollment which is the share of primary school age children that are enrolled in primary school. The next-best indicator is used if ANER is unavailable. In some cases, country specialists will provide a data point that reflects enrollment in the country better than UIS statistics which is used. In future Learning Poverty releases, the enrollment hierarchy may be adjusted as availability of indicator definitions change.\n\n\nThe enrollment year used is the one that best pairs with the assessment year used to compute Learning Deprivation. The year of the preferred assessment is the base. If the same enrollment year is not available, we use a step function to fill the data in with the value of the closest year. If there is data available for two years equally close to the year to fill, the older value is used. This procedure to extrapolate enrollment for missing values is required for us to pair the proficiency measure with enrollment measures from the same year, or its best proxy when enrollment is not available for the same year of the assessment.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading\n\n### Development relevance:\nEnsuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school.\n\n\nThe LP indicator illustrates progress toward SDG 4\u2019s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank\u2019s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school\u2014generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries\u2019 Human Capital Index scores.\n\n### Limitations and exceptions:\nThe enrollment for a given learning poverty release are not strictly comparable between countries, due to the differences in enrollment years and definitions, which are determined by best-match with the assessment year and data availability of enrollment indicators.\n\nThe measure will also differ from out of school estimates using household survey data, which UIS reports for SDG 4.1.4. Households survey estimates are not used for Schooling Deprivation because data is typically reported for countries in various years and with time gaps. School surveys are more feasibly collected annually, while household data collection occurs every few years and can also depend on country demand. School surveys also allow more global consistency as the same survey and source data are used across countries. However, the source used to compute the total school-age population differ in some cases where a country provides their national estimates over the default UNDP population data. However, there are potential trade-offs in precision from school surveys as responses come from school representatives rather than using microdata.\n\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, enrolment rate (any definition) is affected by different age-reference points for enrollment. The length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced. The population data further affects the reference age.\n\nDue to the limitations described, in some cases, country specialists will provide a data point that better reflects enrollment in the country.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS). Indicator SE.LPV.PRIM.SD.FE (https://data.worldbank.org/indicator/SE.LPV.PRIM.SD.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.LPV.PRIM.SD.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_lpv_prim_sd_ma": { "title": "Male primary school age children out-of-school (%)", "description_from_producer": "The share of male children of primary-school age who are out of school or schooling deprived. This dimension is linked to the indicator 4.1.4 from the SDG 4 thematic framework. This element reflects the belief that all primary-age children should be learning in schools of some type, a belief that every country has enshrined in law and that is enshrined in the SDGs. In addition to fulfilling a universal right and serving as a necessary condition for sustained learning, schooling offers many benefits beyond learning. It contributes to children\u2019s health and well-being such as promoting safety, nutrition, and socialization, and facilitating parents' labor market participation and, at the macro level, schooling can help build social cohesion, democracy, and peace. All those complementary functions mean that schooling has value over and above the measured cognitive learning that it leads to, and they justify including schooling deprivation in the concept of learning poverty.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Schooling Deprivation is derived using enrollment rates computed by UIS using administrative records and follows the SDG 4.1.4 indicator. To ensure country coverage of data, we consider other enrollment definitions to use for learning poverty, if the first-best option is not available.\n\n\nWe construct an enrollment dataset from 1990 to the year of the current release, relying on multiple enrollment definitions. Our dataset is constructed from UIS (UNESCO Institute of Statistics), and other sources suggested by World Bank regional or country education specialists. Data sources are typically from administrative records (school registers or school censuses) for data on enrolment by age, and UNPD population estimates for school-age population (UIS). Enrolment by single years of age in all levels of education and the total primary-school-age population are used to compute enrollment rates.\n\n\nWe follow this hierarchy of enrollment definitions: Country specialist validated data > Adjusted Net Enrollment Rate (ANER) > Total Net Enrollment Rate (TNER) > Net enrollment rate (NER) > Gross Enrollment Rate (GER; if the gross enrollment rate is higher than 100%, it is adjusted to be 100%).\n\n\nOur preferred measure of school participation is ANER, because it accounts for some primary school aged children who might enter primary school early and advance to secondary school before they reach the official upper age limit of primary education. Adjusted net enrollment is a measure of both \u201cstock\u201d and \u201cflow\u201d and accounts for both age- and grade-based distortions, as it is the percent of primary school age children enrolled either in primary or secondary education, as opposed to gross enrollment which is the share of children of any age that are enrolled in primary school, or net enrollment which is the share of primary school age children that are enrolled in primary school. The next-best indicator is used if ANER is unavailable. In some cases, country specialists will provide a data point that reflects enrollment in the country better than UIS statistics which is used. In future Learning Poverty releases, the enrollment hierarchy may be adjusted as availability of indicator definitions change.\n\n\nThe enrollment year used is the one that best pairs with the assessment year used to compute Learning Deprivation. The year of the preferred assessment is the base. If the same enrollment year is not available, we use a step function to fill the data in with the value of the closest year. If there is data available for two years equally close to the year to fill, the older value is used. This procedure to extrapolate enrollment for missing values is required for us to pair the proficiency measure with enrollment measures from the same year, or its best proxy when enrollment is not available for the same year of the assessment.\nStatistical concept(s): Minimum Proficiency Level (MPL) in Reading\n\n### Development relevance:\nEnsuring that all students read with comprehension is essential to achieving the ambitious SDG targets and to building human capital. Children need to learn to read so that they can read to learn. Those who do not become proficient in reading by the end of primary school often cannot catch up later, because the curriculum of every school system assumes that secondary-school students can learn through reading. Reading is a gateway to all types of academic learning. In high-income countries, 90% of all children learn to read with comprehension before the end of primary school, and for the highest-performing countries, the figure reaches 97% or more. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school.\n\n\nThe LP indicator illustrates progress toward SDG 4\u2019s broader goal of ensuring inclusive and equitable quality education for all. It particularly highlights progress towards SDG 4.1.1(b) and SDG 4.1.4, which specifies that all children attend primary school and reach at least a minimum proficiency level in reading at the end of primary. The indicator is also aligned with the World Bank\u2019s Human Capital Project, which aims to ensure that children reach their full potential in school and in life. The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school\u2014generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first to achieving this basic building block, and so does improving countries\u2019 Human Capital Index scores.\n\n### Limitations and exceptions:\nThe enrollment for a given learning poverty release are not strictly comparable between countries, due to the differences in enrollment years and definitions, which are determined by best-match with the assessment year and data availability of enrollment indicators.\n\nThe measure will also differ from out of school estimates using household survey data, which UIS reports for SDG 4.1.4. Households survey estimates are not used for Schooling Deprivation because data is typically reported for countries in various years and with time gaps. School surveys are more feasibly collected annually, while household data collection occurs every few years and can also depend on country demand. School surveys also allow more global consistency as the same survey and source data are used across countries. However, the source used to compute the total school-age population differ in some cases where a country provides their national estimates over the default UNDP population data. However, there are potential trade-offs in precision from school surveys as responses come from school representatives rather than using microdata.\n\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, enrolment rate (any definition) is affected by different age-reference points for enrollment. The length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced. The population data further affects the reference age.\n\nDue to the limitations described, in some cases, country specialists will provide a data point that better reflects enrollment in the country.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank (WB);\nUN Educational, Scientific and Cultural Organization (UNESCO), publisher: UNESCO Institute for Statistics (UIS). Indicator SE.LPV.PRIM.SD.MA (https://data.worldbank.org/indicator/SE.LPV.PRIM.SD.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.LPV.PRIM.SD.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_pre_durs": { "title": "Preprimary education, duration (years)", "description_from_producer": "Preprimary duration refers to the number of grades (years) in preprimary school.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the number of years that a country's laws or regulations specify for the preprimary stage of education. The definition of pre-primary education as programs that introduce children, typically starting at the age of 3, to a structured school environment and serve as a transition from home to school aligns with UNESCO's description(https://learningportal.iiep.unesco.org/en/glossary/pre-primary-education).\n\n### Development relevance:\nThe significance of pre-primary education lies in its role in laying a strong groundwork for children's social, emotional, and general well-being. Sustainable Development Goal (SDG) target 4.2 is dedicated to ensuring that all children can participate in high-quality early childhood development, care, and pre-primary education programs. This particular indicator is instrumental in determining the number of children eligible for pre-primary education and is a critical component for the computation of additional indicators, as well as for assessing a nation's ability to fulfill the educational needs at this fundamental level of learning.\n\n### Limitations and exceptions:\nThe presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRE.DURS (https://data.worldbank.org/indicator/SE.PRE.DURS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRE.DURS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "years", "short_unit": "years", "display": { "shortUnit": "years", "unit": "years" } }, "se_pre_enrl_tc_zs": { "title": "Pupil-teacher ratio, preprimary", "description_from_producer": "Preprimary school pupil-teacher ratio is the average number of pupils per teacher in preprimary school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nThe pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education.\n\n### Limitations and exceptions:\nThe comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRE.ENRL.TC.ZS (https://data.worldbank.org/indicator/SE.PRE.ENRL.TC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRE.ENRL.TC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_pre_enrr": { "title": "Gross enrollment in pre-primary education", "description_short": "Number of children of any age group who are enrolled in [pre-primary education](#dod:pre-primary-education), expressed as a percentage of the total population of the official pre-primary school age.", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nGross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "description_key": "- The gross enrollment ratio counts all students enrolled at a specific education level, regardless of their age. This includes students who are younger or older than the official age range because they started school early, started late, or repeated grades.\n- It measures enrollment at a given level by dividing the total number of students\u2014of any age\u2014by the size of the age group officially assigned to that level. For instance, primary enrollment counts everyone enrolled, even outside ages 6\u201311, but compares it only to the 6\u201311 population.\n- Values can exceed 100% when students repeat grades or start late. For instance, primary school enrollment might include 12- or 13-year-olds who started late or repeated grades, pushing the ratio above 100%.\n- A value of 100% does not guarantee that all children in the official age group are enrolled in school; some may be out of school entirely, but the ratio appears high due to older students being counted in the total.\n- Values much lower than 100% indicate that many children of the official age are not enrolled at the expected education level - they may be out of school entirely, enrolled at a different level, or have never started school.\n- Values much higher than 100% can indicate educational challenges such as high grade repetition rates, late school entry, or inefficient progression through the system.\n- The data comes from administrative records such as school enrollment reports, combined with population estimates typically sourced from the United Nations or national statistical offices.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRE.ENRR (https://data.worldbank.org/indicator/SE.PRE.ENRR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRE.ENRR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "numDecimalPlaces": 1, "tolerance": 5, "name": "Pre-primary education", "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "maxTime": 2024, "hasMapTab": true, "map": { "time": 2024, "timeTolerance": 10, "colorScale": { "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 1 ], "baseColorScheme": "PuBuGn" } } }, "title_public": "Gross enrollment in pre-primary education" } }, "se_pre_enrr_fe": { "title": "Gross enrollment in pre-primary education among girls", "description_short": "Number of girls of any age group who are enrolled in [pre-primary education](#dod:pre-primary-education), expressed as a percentage of the total population of girls of the official pre-primary school age.", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nGross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "description_key": "- The gross enrollment ratio counts all students enrolled at a specific education level, regardless of their age. This includes students who are younger or older than the official age range because they started school early, started late, or repeated grades.\n- It measures enrollment at a given level by dividing the total number of students\u2014of any age\u2014by the size of the age group officially assigned to that level. For instance, primary enrollment counts everyone enrolled, even outside ages 6\u201311, but compares it only to the 6\u201311 population.\n- Values can exceed 100% when students repeat grades or start late. For instance, primary school enrollment might include 12- or 13-year-olds who started late or repeated grades, pushing the ratio above 100%.\n- A value of 100% does not guarantee that all children in the official age group are enrolled in school; some may be out of school entirely, but the ratio appears high due to older students being counted in the total.\n- Values much lower than 100% indicate that many children of the official age are not enrolled at the expected education level - they may be out of school entirely, enrolled at a different level, or have never started school.\n- Values much higher than 100% can indicate educational challenges such as high grade repetition rates, late school entry, or inefficient progression through the system.\n- The data comes from administrative records such as school enrollment reports, combined with population estimates typically sourced from the United Nations or national statistical offices.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRE.ENRR.FE (https://data.worldbank.org/indicator/SE.PRE.ENRR.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRE.ENRR.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "numDecimalPlaces": 1, "tolerance": 5, "name": "Girls", "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "maxTime": 2024, "hasMapTab": true, "map": { "time": 2024, "timeTolerance": 10, "colorScale": { "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 1 ], "baseColorScheme": "PuBuGn" } } }, "title_public": "Gross enrollment rate in pre-primary education among girls" } }, "se_pre_enrr_ma": { "title": "Gross enrollment in pre-primary education among boys", "description_short": "Number of boys of any age group who are enrolled in [pre-primary education](#dod:pre-primary-education), expressed as a percentage of the total population of boys of the official pre-primary school age.", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Preprimary education refers to programs at the initial stage of organized instruction, designed primarily to introduce very young children to a school-type environment and to provide a bridge between home and school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for pre-primary school is calculated by dividing the number of students enrolled in pre-primary education regardless of age by the population of the age group which officially corresponds to pre-primary education, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nGross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "description_key": "- The gross enrollment ratio counts all students enrolled at a specific education level, regardless of their age. This includes students who are younger or older than the official age range because they started school early, started late, or repeated grades.\n- It measures enrollment at a given level by dividing the total number of students\u2014of any age\u2014by the size of the age group officially assigned to that level. For instance, primary enrollment counts everyone enrolled, even outside ages 6\u201311, but compares it only to the 6\u201311 population.\n- Values can exceed 100% when students repeat grades or start late. For instance, primary school enrollment might include 12- or 13-year-olds who started late or repeated grades, pushing the ratio above 100%.\n- A value of 100% does not guarantee that all children in the official age group are enrolled in school; some may be out of school entirely, but the ratio appears high due to older students being counted in the total.\n- Values much lower than 100% indicate that many children of the official age are not enrolled at the expected education level - they may be out of school entirely, enrolled at a different level, or have never started school.\n- Values much higher than 100% can indicate educational challenges such as high grade repetition rates, late school entry, or inefficient progression through the system.\n- The data comes from administrative records such as school enrollment reports, combined with population estimates typically sourced from the United Nations or national statistical offices.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRE.ENRR.MA (https://data.worldbank.org/indicator/SE.PRE.ENRR.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRE.ENRR.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "numDecimalPlaces": 1, "tolerance": 5, "name": "Boys", "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "maxTime": 2024, "hasMapTab": true, "map": { "time": 2024, "timeTolerance": 10, "colorScale": { "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 1 ], "baseColorScheme": "PuBuGn" } } }, "title_public": "Gross enrollment rate in pre-primary education among boys" } }, "se_pre_tcaq_fe_zs": { "title": "Trained teachers in preprimary education, female (% of female teachers)", "description_from_producer": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRE.TCAQ.FE.ZS (https://data.worldbank.org/indicator/SE.PRE.TCAQ.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRE.TCAQ.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female teachers", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female teachers" } }, "se_pre_tcaq_ma_zs": { "title": "Trained teachers in preprimary education, male (% of male teachers)", "description_from_producer": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRE.TCAQ.MA.ZS (https://data.worldbank.org/indicator/SE.PRE.TCAQ.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRE.TCAQ.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male teachers", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male teachers" } }, "se_pre_tcaq_zs": { "title": "Trained teachers in pre-primary education (% of total teachers)", "description_short": "Share of teachers in [pre-primary education](#dod:pre-primary-education) who have received at least the minimum organized pedagogical training required for teaching at that level, shown as a percentage of all teachers.", "description_from_producer": "Trained teachers in preprimary education are the percentage of preprimary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "description_key": "- Not all teachers receive the same level of preparation before entering the classroom. This indicator measures the percentage of teachers at each education level who have completed at least the minimum organized pedagogical training required for teaching at that level in their country.\n- It captures the share of teachers who are pedagogically trained, expressed as a percentage of all teachers at that level. For example, if there are 1,000 primary teachers and 850 have completed the required training, the indicator shows 85%.\n- The \"minimum training\" refers to whatever organized pedagogical preparation each country requires \u2014 pre-service teacher education programs, in-service training courses, or certification requirements that focus on teaching methods rather than subject knowledge.\n- Values below 100% suggest some teachers are working without the pedagogical training their own country considers necessary. However, cross-country comparisons can be misleading since training requirements vary significantly between nations.\n- This indicator does not account for differences in teachers' experience, professional status, teaching methods, materials, or classroom conditions \u2014 all of which affect the quality of teaching and learning. Some teachers without formal training may also acquire equivalent skills through professional experience.\n- The reference years reflect the school year. In countries where the school year spans two calendar years (e.g. September 2010 to June 2011), the reference year is the year in which the school year ended (2011 in this example).\n- The data comes from administrative records collected at schools and other organized learning centers, typically including information on teacher academic credentials and employment status.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRE.TCAQ.ZS (https://data.worldbank.org/indicator/SE.PRE.TCAQ.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRE.TCAQ.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of teachers", "short_unit": "%", "display": { "numDecimalPlaces": 1, "tolerance": 5, "name": "Pre-primary", "shortUnit": "%", "unit": "% of teachers" }, "presentation": { "grapher_config": { "maxTime": 2024, "hasMapTab": true, "map": { "time": 2024, "timeTolerance": 10, "colorScale": { "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 ], "baseColorScheme": "Blues" } } }, "title_public": "Share of trained teachers in pre-primary education", "topic_tags": [ "Global Education" ] } }, "se_prm_ages": { "title": "Primary school starting age (years)", "description_from_producer": "Primary school starting age is the age at which students would enter primary education, assuming they had started at the official entrance age for the lowest level of education, had studied full-time throughout and had progressed through the system without repeating or skipping a grade.\n\n### Statistical concept and methodology:\nMethodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator provides insights into the demand for educational services across various educational levels. Additionally, it serves as a crucial data point necessary for the generation of numerous educational indicators.\n\n### Development relevance:\nEducation is recognized as an essential human right that must be afforded to all individuals. Primary education serves as the cornerstone for the development of fundamental literacy and numeracy skills, which are crucial for a solid foundation in the learning process. It also plays a significant role in promoting personal and social development. The importance of primary education is underscored by Sustainable Development Goal (SDG) target 4.1, which advocates for inclusive and equitable quality education at the primary level for all children.\n\n### Limitations and exceptions:\nThe theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.AGES (https://data.worldbank.org/indicator/SE.PRM.AGES). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.AGES", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "years", "short_unit": "years", "display": { "shortUnit": "years", "unit": "years" } }, "se_prm_cmpt_fe_zs": { "title": "Primary completion rate, female (% of relevant age group)", "description_from_producer": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education.\n\n### Development relevance:\nPrimary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education.\n\n### Limitations and exceptions:\nThe data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.CMPT.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.CMPT.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.CMPT.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of relevant age group", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of relevant age group" } }, "se_prm_cmpt_ma_zs": { "title": "Primary completion rate, male (% of relevant age group)", "description_from_producer": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education.\n\n### Development relevance:\nPrimary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education.\n\n### Limitations and exceptions:\nThe data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.CMPT.MA.ZS (https://data.worldbank.org/indicator/SE.PRM.CMPT.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.CMPT.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of relevant age group", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of relevant age group" } }, "se_prm_cmpt_zs": { "title": "Primary completion rate, total (% of relevant age group)", "description_from_producer": "Primary completion rate, or gross intake ratio to the last grade of primary education, is the number of new entrants (enrollments minus repeaters) in the last grade of primary education, regardless of age, divided by the population at the entrance age for the last grade of primary education. Data limitations preclude adjusting for students who drop out during the final year of primary education.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Primary completion rate is calculated by dividing the number of new entrants (enrollment minus repeaters) in the last grade of primary education, regardless of age, by the population at the entrance age for the last grade of primary education and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education.\n\n### Development relevance:\nPrimary education lays the groundwork for acquiring essential literacy and numeracy skills, setting the stage for a robust learning journey and fostering overall personal and social growth. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator holds significant relevance for policy-makers dedicated to enhancing children's educational access and engagement. It gauges the capacity of the education system to support a group of students from their expected entry age to the completion of all grades of primary education.\n\n### Limitations and exceptions:\nThe data do not account for students who drop out during the final year of primary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual primary completion rate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.CMPT.ZS (https://data.worldbank.org/indicator/SE.PRM.CMPT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.CMPT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of relevant age group", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of relevant age group" } }, "se_prm_cuat_fe_zs": { "title": "Educational attainment, at least completed primary, population 25+ years, female (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed primary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital?\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.CUAT.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.CUAT.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.CUAT.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_prm_cuat_ma_zs": { "title": "Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed primary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.CUAT.MA.ZS (https://data.worldbank.org/indicator/SE.PRM.CUAT.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.CUAT.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_prm_cuat_zs": { "title": "Educational attainment, at least completed primary, population 25+ years, total (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed primary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed primary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relatively high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.CUAT.ZS (https://data.worldbank.org/indicator/SE.PRM.CUAT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.CUAT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_prm_durs": { "title": "Primary education, duration (years)", "description_from_producer": "Primary duration refers to the number of grades (years) in primary school.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the number of years that a country's laws or regulations specify for primary education. It aids in identifying the population of school-aged children at different educational levels. Additionally, this metric serves as crucial input data necessary for generating various indicators and evaluating a country's capacity to meet educational demand.\n\n### Development relevance:\nPrimary education serves as the cornerstone for the development of fundamental literacy and numeracy skills, which are crucial for a solid foundation in the learning process. It also plays a significant role in promoting personal and social development. The importance of primary education is underscored by Sustainable Development Goal (SDG) target 4.1, which advocates completion of inclusive and equitable quality education at the primary level for all children.\n\n### Limitations and exceptions:\nThe presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.DURS (https://data.worldbank.org/indicator/SE.PRM.DURS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.DURS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "years", "short_unit": "years", "display": { "shortUnit": "years", "unit": "years" } }, "se_prm_enrl": { "title": "Primary education, pupils", "description_from_producer": "Primary education pupils is the total number of pupils enrolled at primary level in public and private schools.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Educational programs at the International Standard Classification of Education (ISCED) level 1, commonly referred to as primary education, are fundamentally structured to equip students with essential literacy and numeracy skills. These programs aim to lay a robust groundwork for learning, fostering an understanding of key knowledge domains, and promoting personal and social growth, thereby readying students for the subsequent stage of lower secondary education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEntry into this level is predominantly based on age, with the typical or legally mandated age of enrollment ranging from 5 to 7 years old. The duration of primary education is generally six years, though it can vary from four to seven years, concluding when students are between 10 to 12 years old (refer to Paragraphs 132 to 134 for further details). Upon successful completion of primary education, students are eligible to progress to ISCED level 2, which corresponds to lower secondary education.\n\n### Development relevance:\nPrimary education is a fundamental human right. It serves as the cornerstone for subsequent achievement and creates opportunities for further progress. Its extensive influence extends to individuals and the collective society, playing a critical role in the alleviation of extreme poverty, the promotion of health, and the advancement of gender equality. The SDG target 4.1 advocates for the completion of free, equitable, and high-quality primary and secondary education for all children, aiming to achieve meaningful and impactful learning outcomes by the year 2030.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.ENRL (https://data.worldbank.org/indicator/SE.PRM.ENRL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.ENRL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_prm_enrl_fe_zs": { "title": "Primary education, pupils (% female)", "description_from_producer": "Female pupils as a percentage of total pupils at primary level include enrollments in public and private schools.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nThe share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education.\n\n### Limitations and exceptions:\nThe percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.ENRL.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.ENRL.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.ENRL.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% female", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% female" } }, "se_prm_enrl_tc_zs": { "title": "Pupil-teacher ratio, primary", "description_from_producer": "Primary school pupil-teacher ratio is the average number of pupils per teacher in primary school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education.\n\n### Limitations and exceptions:\nThe comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.ENRL.TC.ZS (https://data.worldbank.org/indicator/SE.PRM.ENRL.TC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.ENRL.TC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_prm_enrr": { "title": "School enrollment, primary (% gross)", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system.\n\n### Development relevance:\nPrimary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.ENRR (https://data.worldbank.org/indicator/SE.PRM.ENRR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.ENRR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% gross", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% gross" } }, "se_prm_enrr_fe": { "title": "School enrollment, primary, female (% gross)", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system.\n\n### Development relevance:\nPrimary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.ENRR.FE (https://data.worldbank.org/indicator/SE.PRM.ENRR.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.ENRR.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% gross", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% gross" } }, "se_prm_enrr_ma": { "title": "School enrollment, primary, male (% gross)", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for primary school is calculated by dividing the number of students enrolled in primary education regardless of age by the population of the age group which officially corresponds to primary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system.\n\n### Development relevance:\nPrimary education is fundamental to future educational success and opens pathways for continued advancement. This indicator measures the overall rate of participation in primary education, signifying the education system's ability to enroll students within a specific age cohort.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.ENRR.MA (https://data.worldbank.org/indicator/SE.PRM.ENRR.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.ENRR.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% gross", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% gross" } }, "se_prm_gint_fe_zs": { "title": "Gross intake ratio in first grade of primary education, female (% of relevant age group)", "description_from_producer": "Gross intake ratio in first grade of primary education is the number of new entrants in the first grade of primary education regardless of age, expressed as a percentage of the population of the official primary entrance age.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross intake ratio in the first grade of primary education is calculated by dividing the number of new entrants (enrollments minus repeaters) in the first grade of primary education, regardless of age, by the population of the official primary entrance age and multiplying the result by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe gross intake ratio in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A low gross intake ratio in the first grade of primary education reflects the fact that many children do not enter primary education even though school attendance, at least through the primary level, is mandatory in most countries. Because the gross intake ratio includes all new entrants regardless of age, it can exceed 100 percent in some situations, such as immediately after fees have been abolished or when the number of reenrolled children is large.\n\n### Limitations and exceptions:\nThe quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.GINT.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.GINT.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.GINT.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of relevant age group", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of relevant age group" } }, "se_prm_gint_ma_zs": { "title": "Gross intake ratio in first grade of primary education, male (% of relevant age group)", "description_from_producer": "Gross intake ratio in first grade of primary education is the number of new entrants in the first grade of primary education regardless of age, expressed as a percentage of the population of the official primary entrance age.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross intake ratio in the first grade of primary education is calculated by dividing the number of new entrants (enrollments minus repeaters) in the first grade of primary education, regardless of age, by the population of the official primary entrance age and multiplying the result by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe gross intake ratio in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A low gross intake ratio in the first grade of primary education reflects the fact that many children do not enter primary education even though school attendance, at least through the primary level, is mandatory in most countries. Because the gross intake ratio includes all new entrants regardless of age, it can exceed 100 percent in some situations, such as immediately after fees have been abolished or when the number of reenrolled children is large.\n\n### Limitations and exceptions:\nThe quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.GINT.MA.ZS (https://data.worldbank.org/indicator/SE.PRM.GINT.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.GINT.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of relevant age group", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of relevant age group" } }, "se_prm_gint_zs": { "title": "Gross intake ratio in first grade of primary education, total (% of relevant age group)", "description_from_producer": "Gross intake ratio in first grade of primary education is the number of new entrants in the first grade of primary education regardless of age, expressed as a percentage of the population of the official primary entrance age.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross intake ratio in the first grade of primary education is calculated by dividing the number of new entrants (enrollments minus repeaters) in the first grade of primary education, regardless of age, by the population of the official primary entrance age and multiplying the result by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe gross intake ratio in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A low gross intake ratio in the first grade of primary education reflects the fact that many children do not enter primary education even though school attendance, at least through the primary level, is mandatory in most countries. Because the gross intake ratio includes all new entrants regardless of age, it can exceed 100 percent in some situations, such as immediately after fees have been abolished or when the number of reenrolled children is large.\n\n### Limitations and exceptions:\nThe quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.GINT.ZS (https://data.worldbank.org/indicator/SE.PRM.GINT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.GINT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of relevant age group", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of relevant age group" } }, "se_prm_nenr": { "title": "School enrollment, primary (% net)", "description_from_producer": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nGross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.NENR (https://data.worldbank.org/indicator/SE.PRM.NENR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.NENR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% net", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% net" } }, "se_prm_nenr_fe": { "title": "School enrollment, primary, female (% net)", "description_from_producer": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nGross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNESCO Institute for Statistics (UIS), UN Educational, Scientific and Cultural Organization (UNESCO), uri: http://uis.unesco.org/, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.NENR.FE (https://data.worldbank.org/indicator/SE.PRM.NENR.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.NENR.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% net", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% net" } }, "se_prm_nenr_ma": { "title": "School enrollment, primary, male (% net)", "description_from_producer": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Primary education provides children with basic reading, writing, and mathematics skills along with an elementary understanding of such subjects as history, geography, natural science, social science, art, and music.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net enrollment rate for primary school is calculated by dividing the number of students of official school age enrolled in primary education by the population of the age group which officially corresponds to primary education, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nGross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.NENR.MA (https://data.worldbank.org/indicator/SE.PRM.NENR.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.NENR.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% net", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% net" } }, "se_prm_nint_fe_zs": { "title": "Net intake rate in grade 1, female (% of official school-age population)", "description_from_producer": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children.\n\n### Limitations and exceptions:\nThe quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.NINT.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.NINT.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.NINT.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of official school-age population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of official school-age population" } }, "se_prm_nint_ma_zs": { "title": "Net intake rate in grade 1, male (% of official school-age population)", "description_from_producer": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children.\n\n### Limitations and exceptions:\nThe quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.NINT.MA.ZS (https://data.worldbank.org/indicator/SE.PRM.NINT.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.NINT.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of official school-age population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of official school-age population" } }, "se_prm_nint_zs": { "title": "Net intake rate in grade 1 (% of official school-age population)", "description_from_producer": "Net intake rate in grade 1 is the number of new entrants in the first grade of primary education who are of official primary school entrance age, expressed as a percentage of the population of the corresponding age.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net intake rate in the first grade of primary education is calculated by dividing the number of children of official primary school entrance age who enter grade 1 of primary education for the first time by the population of the same age, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nThe net intake rate in the first grade of primary education indicates the level of access to primary education and the education system's capacity to provide access to primary education. A high net intake rate indicates a high degree of access to primary education for the official primary school entrance age children.\n\n### Limitations and exceptions:\nThe quality of data is affected when new entrants and repeaters are not correctly distinguished in the first grade of primary education. Caution is also needed for countries with a total population under 100,000 since the United Nations Population Division neither publish nor endorse single-age data for those countries. The data are highly subject to fluctuations in migration and other factors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.NINT.ZS (https://data.worldbank.org/indicator/SE.PRM.NINT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.NINT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of official school-age population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of official school-age population" } }, "se_prm_oenr_fe_zs": { "title": "Over-age students, primary, female (% of female enrollment)", "description_from_producer": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education.\n\n### Statistical concept and methodology:\nMethodology: The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.OENR.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.OENR.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.OENR.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female enrollment", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female enrollment" } }, "se_prm_oenr_ma_zs": { "title": "Over-age students, primary, male (% of male enrollment)", "description_from_producer": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education.\n\n### Statistical concept and methodology:\nMethodology: The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.OENR.MA.ZS (https://data.worldbank.org/indicator/SE.PRM.OENR.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.OENR.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male enrollment", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male enrollment" } }, "se_prm_oenr_zs": { "title": "Over-age students, primary (% of enrollment)", "description_from_producer": "Over-age students are the percentage of those enrolled who are older than the official school-age range for primary education.\n\n### Statistical concept and methodology:\nMethodology: The percentage of over-age students is calculated by dividing the number of students who are older than the official school-age range for primary education by primary school enrollment, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.OENR.ZS (https://data.worldbank.org/indicator/SE.PRM.OENR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.OENR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of enrollment", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of enrollment" } }, "se_prm_priv_zs": { "title": "School enrollment, primary, private (% of total primary)", "description_from_producer": "Private enrollment refers to pupils or students enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body such as a nongovernmental organization, religious body, special interest group, foundation or business enterprise.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The share of students in private primary school is calculated by dividing the number of students enrolled in private educational institutions at primary level by total enrollment (public and private) at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of enrollment in private institutions indicates the scale and capacity of private education within a country. A high percentage suggests strong involvement of the non-governmental sector (including religious bodies, other organizations, associations, communities, private enterprises or persons) in providing organized educational programmes. However, in countries where private institutions are substantially subsidized or aided by the government, the distinction between private and public educational institutions may be less clear-cut especially when certain students are directly financed through government scholarships.\n\n### Development relevance:\nThe indicator reflects the proportion of students attending private educational institutions. Globally, private schools educate approximately 350 million children, and there has been a notable rise in the prevalence of private institutions (https://world-education-blog.org/2021/12/10/new-2021-2-gem-report-out-today-who-chooses-who-loses/). UNESCO emphasizes the importance of comprehensively understanding the contexts and frameworks within which both public and private schools function in each nation. This understanding is vital to guarantee that children's right to education is upheld and that their specific educational requirements are addressed.\n\n### Limitations and exceptions:\nReligious or private schools, which are not registered with the government or don't follow the common national curriculum, may not be captured.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.PRIV.ZS (https://data.worldbank.org/indicator/SE.PRM.PRIV.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.PRIV.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total primary", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total primary" } }, "se_prm_prs5_fe_zs": { "title": "Persistence to grade 5, female (% of cohort)", "description_from_producer": "Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels.\n\n### Development relevance:\nSustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level.\n\n### Limitations and exceptions:\nThe estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.PRS5.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.PRS5.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.PRS5.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of cohort", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of cohort" } }, "se_prm_prs5_ma_zs": { "title": "Persistence to grade 5, male (% of cohort)", "description_from_producer": "Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels.\n\n### Development relevance:\nSustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level.\n\n### Limitations and exceptions:\nThe estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.PRS5.MA.ZS (https://data.worldbank.org/indicator/SE.PRM.PRS5.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.PRS5.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of cohort", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of cohort" } }, "se_prm_prs5_zs": { "title": "Persistence to grade 5, total (% of cohort)", "description_from_producer": "Persistence to grade 5 (percentage of cohort reaching grade 5) is the share of children enrolled in the first grade of primary school who eventually reach grade 5. The estimate is based on the reconstructed cohort method.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels.\n\n### Development relevance:\nSustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level.\n\n### Limitations and exceptions:\nThe estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.PRS5.ZS (https://data.worldbank.org/indicator/SE.PRM.PRS5.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.PRS5.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of cohort", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of cohort" } }, "se_prm_prsl_fe_zs": { "title": "Persistence to last grade of primary, female (% of cohort)", "description_from_producer": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education.\n\n### Development relevance:\nSustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level.\n\n### Limitations and exceptions:\nThe estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.PRSL.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.PRSL.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.PRSL.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of cohort", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of cohort" } }, "se_prm_prsl_ma_zs": { "title": "Persistence to last grade of primary, male (% of cohort)", "description_from_producer": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education.\n\n### Development relevance:\nSustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level.\n\n### Limitations and exceptions:\nThe estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.PRSL.MA.ZS (https://data.worldbank.org/indicator/SE.PRM.PRSL.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.PRSL.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of cohort", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of cohort" } }, "se_prm_prsl_zs": { "title": "Persistence to last grade of primary, total (% of cohort)", "description_from_producer": "Persistence to last grade of primary is the percentage of children enrolled in the first grade of primary school who eventually reach the last grade of primary education. The estimate is based on the reconstructed cohort method.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Cohort survival rate is calculated by dividing the total number of children belonging to a cohort who reached each successive grade of the specified level of education by the number of children in the same cohort; those originally enrolled in the first grade of primary education, and multiplying by 100. To reflect current patterns of grade transition, it is calculated based on the reconstructed cohort method, which uses data on enrollment by grade for the two most recent years and data on repeaters by grade for the most recent of those two years.\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The cohort survival rate measures an education system's holding power and internal efficiency. Rates approaching 100 percent indicate high retention and low dropout levels. Survival rate to the last grade of primary education is of particular interest for monitoring universal primary education.\n\n### Development relevance:\nSustainable Development Goal (SDG) target 4.1 is committed to ensuring that all girls and boys complete a cycle of free, equitable, and high-quality primary education. Despite this commitment, numerous children in low-income countries are unable to finish their primary schooling. This indicator serves as a measure of an education system's ability to retain students from one grade to the next, thereby reflecting the system's internal efficiency. It also highlights the extent of student dropout rates at each grade level.\n\n### Limitations and exceptions:\nThe estimates have limitations in capturing real trend in that an observed rate will be applied to the underlying indicators such as repetition rate and promotion rate throughout the cohort life, and re-entrants, grade skipping, migration or transfers during a school year are not adequately captured.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.PRSL.ZS (https://data.worldbank.org/indicator/SE.PRM.PRSL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.PRSL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of cohort", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of cohort" } }, "se_prm_rept_fe_zs": { "title": "Repeaters, primary, female (% of female enrollment)", "description_from_producer": "Repeaters in primary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in primary school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of repeaters in primary school is calculated by dividing the sum of repeaters in all grades of primary school by the total number of students enrolled in primary school, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nData on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources.\n\n### Limitations and exceptions:\nCountry policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion \u2013 suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.REPT.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.REPT.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.REPT.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female enrollment", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female enrollment" } }, "se_prm_rept_ma_zs": { "title": "Repeaters, primary, male (% of male enrollment)", "description_from_producer": "Repeaters in primary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in primary school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of repeaters in primary school is calculated by dividing the sum of repeaters in all grades of primary school by the total number of students enrolled in primary school, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nData on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources.\n\n### Limitations and exceptions:\nCountry policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion \u2013 suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.REPT.MA.ZS (https://data.worldbank.org/indicator/SE.PRM.REPT.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.REPT.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male enrollment", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male enrollment" } }, "se_prm_rept_zs": { "title": "Repeaters, primary, total (% of total enrollment)", "description_from_producer": "Repeaters in primary school are the number of students enrolled in the same grade as in the previous year, as a percentage of all students enrolled in primary school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of repeaters in primary school is calculated by dividing the sum of repeaters in all grades of primary school by the total number of students enrolled in primary school, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nData on repeaters are often used to indicate an education system's internal efficiency. Repeaters not only increase the cost of education for the family and the school system, but also use limited school resources.\n\n### Limitations and exceptions:\nCountry policies on repetition and promotion differ. In some cases the number of repeaters is controlled because of limited capacity. In other cases the number of repeaters is almost 0 because of automatic promotion \u2013 suggesting a system that is highly efficient but that may not be endowing students with enough cognitive skills.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.REPT.ZS (https://data.worldbank.org/indicator/SE.PRM.REPT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.REPT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total enrollment", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total enrollment" } }, "se_prm_tcaq_fe_zs": { "title": "Trained teachers in primary education, female (% of female teachers)", "description_from_producer": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.TCAQ.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.TCAQ.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.TCAQ.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female teachers", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female teachers" } }, "se_prm_tcaq_ma_zs": { "title": "Trained teachers in primary education, male (% of male teachers)", "description_from_producer": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.TCAQ.MA.ZS (https://data.worldbank.org/indicator/SE.PRM.TCAQ.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.TCAQ.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male teachers", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male teachers" } }, "se_prm_tcaq_zs": { "title": "Trained teachers in primary education (% of total teachers)", "description_short": "Share of teachers in [primary education](#dod:primary-education) who have received at least the minimum organized pedagogical training required for teaching at that level, shown as a percentage of all teachers.", "description_from_producer": "Trained teachers in primary education are the percentage of primary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "description_key": "- Not all teachers receive the same level of preparation before entering the classroom. This indicator measures the percentage of teachers at each education level who have completed at least the minimum organized pedagogical training required for teaching at that level in their country.\n- It captures the share of teachers who are pedagogically trained, expressed as a percentage of all teachers at that level. For example, if there are 1,000 primary teachers and 850 have completed the required training, the indicator shows 85%.\n- The \"minimum training\" refers to whatever organized pedagogical preparation each country requires \u2014 pre-service teacher education programs, in-service training courses, or certification requirements that focus on teaching methods rather than subject knowledge.\n- Values below 100% suggest some teachers are working without the pedagogical training their own country considers necessary. However, cross-country comparisons can be misleading since training requirements vary significantly between nations.\n- This indicator does not account for differences in teachers' experience, professional status, teaching methods, materials, or classroom conditions \u2014 all of which affect the quality of teaching and learning. Some teachers without formal training may also acquire equivalent skills through professional experience.\n- The reference years reflect the school year. In countries where the school year spans two calendar years (e.g. September 2010 to June 2011), the reference year is the year in which the school year ended (2011 in this example).\n- The data comes from administrative records collected at schools and other organized learning centers, typically including information on teacher academic credentials and employment status.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.TCAQ.ZS (https://data.worldbank.org/indicator/SE.PRM.TCAQ.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.TCAQ.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of teachers", "short_unit": "%", "display": { "numDecimalPlaces": 1, "tolerance": 5, "name": "Primary", "shortUnit": "%", "unit": "% of teachers" }, "presentation": { "grapher_config": { "maxTime": 2024, "hasMapTab": true, "map": { "time": 2024, "timeTolerance": 10, "colorScale": { "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 ], "baseColorScheme": "Blues" } } }, "title_public": "Share of trained teachers in primary education", "topic_tags": [ "Global Education" ] } }, "se_prm_tchr": { "title": "Primary education, teachers", "description_from_producer": "Primary education, teachers refers to the total number of teachers at primary level, including full-time and part-time teachers.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Teachers refer to persons employed full-time or part-time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) or who work occasionally or in a voluntary capacity in educational institutions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The metric of personnel count primarily engaged in teaching and/or research reflects the scale, diversity, and distribution of educational staff within a nation's academic institutions. An increased count is anticipated to enrich the educational setting by means of dedicated teaching, practical research, academic pursuits, and contributions to the service of national educational policies.\n\n### Development relevance:\nTeachers are pivotal in molding the future and unleashing the potential of each student. The United Nations Educational, Scientific and Cultural Organization (UNESCO) highlights a global teacher shortage as a significant obstacle in realizing Sustainable Development Goal 4, which aims for inclusive and equitable quality education.\n\n### Limitations and exceptions:\nThe precision of this indicator can be influenced by the enumeration method used, such as headcount or 'full-time equivalent' count of teachers.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.TCHR (https://data.worldbank.org/indicator/SE.PRM.TCHR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.TCHR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_prm_tchr_fe_zs": { "title": "Primary education, teachers (% female)", "description_from_producer": "Female teachers as a percentage of total primary education teachers includes full-time and part-time teachers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The share of female teachers in primary education is calculated by dividing the total number of female teachers at primary level of education by the total number of teachers at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities.\n\n### Development relevance:\nAlthough there have been advancements, girls in low-income countries continue to face significant barriers to accessing secondary education. The presence of female teachers is crucial in this context, as they act as role models, inspiring and motivating girls to pursue their education. These educators play a pivotal role in attracting and retaining girls in schools, challenging deep-seated gender stereotypes within communities, elevating parental expectations for their daughters, and contributing to the narrowing of the educational achievement gap between boys and girls.\n\n### Limitations and exceptions:\nThe indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.TCHR.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.TCHR.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.TCHR.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% female", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% female" } }, "se_prm_tenr": { "title": "Adjusted net enrollment rate, primary (% of primary school age children)", "description_from_producer": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nRelevance to gender indicator: Women teachers are important as they serve as role models to girls and help to attract and retain girls in school.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.TENR (https://data.worldbank.org/indicator/SE.PRM.TENR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.TENR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of primary school age children", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of primary school age children" } }, "se_prm_tenr_fe": { "title": "Adjusted net enrollment rate, primary, female (% of primary school age children)", "description_from_producer": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nRelevance to gender indicator: Women teachers are important as they serve as role models to girls and help to attract and retain girls in school.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.TENR.FE (https://data.worldbank.org/indicator/SE.PRM.TENR.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.TENR.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of primary school age children", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of primary school age children" } }, "se_prm_tenr_ma": { "title": "Adjusted net enrollment rate, primary, male (% of primary school age children)", "description_from_producer": "Adjusted net enrollment is the number of pupils of the school-age group for primary education, enrolled either in primary or secondary education, expressed as a percentage of the total population in that age group.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Adjusted net enrollment rate in primary education is calculated by dividing the number of children in the official primary school age who are enrolled in primary or secondary education by the population of the same age group and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nGross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments. The adjusted net enrollment rate in primary education captures primary school-age children who have progressed to secondary education faster than their peers have and who are not counted in the traditional net enrollment rate.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.PRM.TENR.MA (https://data.worldbank.org/indicator/SE.PRM.TENR.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.TENR.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of primary school age children", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of primary school age children" } }, "se_prm_uner": { "title": "Children out of school, primary", "description_from_producer": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the population of children within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level.\n\n### Development relevance:\nEnsuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility.\n\n### Limitations and exceptions:\nDue to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.UNER (https://data.worldbank.org/indicator/SE.PRM.UNER). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.UNER", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_prm_uner_fe": { "title": "Children out of school, primary, female", "description_from_producer": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the population of children within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level.\n\n### Development relevance:\nEnsuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility.\n\n### Limitations and exceptions:\nDue to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.UNER.FE (https://data.worldbank.org/indicator/SE.PRM.UNER.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.UNER.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_prm_uner_fe_zs": { "title": "Children out of school, female (% of female primary school age)", "description_from_producer": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level.\n\n### Development relevance:\nEnsuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility.\n\n### Limitations and exceptions:\nThe administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.UNER.FE.ZS (https://data.worldbank.org/indicator/SE.PRM.UNER.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.UNER.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female primary school age", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female primary school age" } }, "se_prm_uner_ma": { "title": "Children out of school, primary, male", "description_from_producer": "Children out of school are the number of primary-school-age children not enrolled in primary or secondary school.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The number of out-of-school children is calculated by subtracting the number of primary school-age children enrolled in primary or secondary school from the total population of the official primary school-age children.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the population of children within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level.\n\n### Development relevance:\nEnsuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility.\n\n### Limitations and exceptions:\nDue to different data sources for enrollment and population data, the number may not capture the actual number of children not attending in primary school.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.UNER.MA (https://data.worldbank.org/indicator/SE.PRM.UNER.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.UNER.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_prm_uner_ma_zs": { "title": "Children out of school, male (% of male primary school age)", "description_from_producer": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level.\n\n### Development relevance:\nEnsuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility.\n\n### Limitations and exceptions:\nThe administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.UNER.MA.ZS (https://data.worldbank.org/indicator/SE.PRM.UNER.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.UNER.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male primary school age", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male primary school age" } }, "se_prm_uner_zs": { "title": "Children out of school (% of primary school age)", "description_from_producer": "Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The rate of out-of-school children allows to compare across countries with different population sizes. It shows the share of official primary-school-age children who never attended school or dropped out to the population of official primary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level.\n\n### Development relevance:\nEnsuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility.\n\n### Limitations and exceptions:\nThe administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.PRM.UNER.ZS (https://data.worldbank.org/indicator/SE.PRM.UNER.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.PRM.UNER.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of primary school age", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of primary school age" } }, "se_sec_ages": { "title": "Lower secondary school starting age (years)", "description_from_producer": "Lower secondary school starting age is the age at which students would enter lower secondary education, assuming they had started at the official entrance age for the lowest level of education, had studied full-time throughout and had progressed through the system without repeating or skipping a grade.\n\n### Statistical concept and methodology:\nMethodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator provides insights into the demand for educational services across various educational levels. Additionally, it serves as a crucial data point necessary for the generation of numerous educational indicators.\n\n### Development relevance:\nEducation is recognized as an essential human right that must be afforded to all individuals. Secondary education imparts critical skills that enable individuals to engage fully in societal activities. Sustainable Development Goal (SDG) target 4.1 highlights the significance of secondary education by promoting inclusive and equitable quality education at this level for all youth.\n\n### Limitations and exceptions:\nThe theoretical entrance age to a given programme or level is typically, but not always, the most common entrance age.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.AGES (https://data.worldbank.org/indicator/SE.SEC.AGES). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.AGES", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "years", "short_unit": "years", "display": { "shortUnit": "years", "unit": "years" } }, "se_sec_cmpt_lo_fe_zs": { "title": "Lower secondary completion rate, female (% of relevant age group)", "description_from_producer": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education.\n\n### Development relevance:\nLower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education.\n\n### Limitations and exceptions:\nThe data do not account for students who drop out during the final year of Lower secondary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual Lower secondary completion rate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CMPT.LO.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of relevant age group", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of relevant age group" } }, "se_sec_cmpt_lo_ma_zs": { "title": "Lower secondary completion rate, male (% of relevant age group)", "description_from_producer": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education.\n\n### Development relevance:\nLower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education.\n\n### Limitations and exceptions:\nThe data do not account for students who drop out during the final year of Lower secondary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual Lower secondary completion rate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CMPT.LO.MA.ZS (https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of relevant age group", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of relevant age group" } }, "se_sec_cmpt_lo_zs": { "title": "Lower secondary completion rate, total (% of relevant age group)", "description_from_producer": "Lower secondary education completion rate is measured as the gross intake ratio to the last grade of lower secondary education (general and pre-vocational). It is calculated as the number of new entrants in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Lower secondary completion rate is calculated as the number of new entrants (enrollment minus repeaters) in the last grade of lower secondary education, regardless of age, divided by the population at the entrance age for the last grade of lower secondary education.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The indicator represents the influence of policies regarding entry and progress through the initial stages of primary or lower secondary education on the completion of the terminal grade at the respective level. The presumption is that students who enroll in the final grade for the first time will successfully finish that grade, thereby completing the designated level of education.\n\n### Development relevance:\nLower secondary education serves as a critical platform for lifelong learning and human development, providing a foundation for further educational pursuits. In certain systems, it includes vocational education programs that equip individuals with skills pertinent to the workforce. SDG target 4.1 aims to ensure that all children complete free, equitable, and quality primary education. This indicator is particularly important for policymakers who are committed to improving children's access to and participation in education. It measures the ability of the education system to enroll and retain students from the designated starting age through to the completion of all levels of lower secondary education.\n\n### Limitations and exceptions:\nThe data do not account for students who drop out during the final year of Lower secondary education. Therefore, this rate serves as a proxy and should be considered an upper estimate of the actual Lower secondary completion rate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are many reasons why the primary completion rate can exceed 100 percent. The numerator may include late entrants and overage children who have repeated one or more grades of primary education as well as children who entered school early, while the denominator is the number of children at the entrance age for the last grade of primary education.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CMPT.LO.ZS (https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CMPT.LO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of relevant age group", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of relevant age group" } }, "se_sec_cuat_lo_fe_zs": { "title": "Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed lower secondary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relatively high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CUAT.LO.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_sec_cuat_lo_ma_zs": { "title": "Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed lower secondary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relatively high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CUAT.LO.MA.ZS (https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_sec_cuat_lo_zs": { "title": "Educational attainment, at least completed lower secondary, population 25+, total (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed lower secondary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed lower secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CUAT.LO.ZS (https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CUAT.LO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_sec_cuat_po_fe_zs": { "title": "Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relatively high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CUAT.PO.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_sec_cuat_po_ma_zs": { "title": "Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CUAT.PO.MA.ZS (https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_sec_cuat_po_zs": { "title": "Educational attainment, at least completed post-secondary, population 25+, total (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed post-secondary non-tertiary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed post-secondary non-tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CUAT.PO.ZS (https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CUAT.PO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_sec_cuat_up_fe_zs": { "title": "Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed upper secondary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CUAT.UP.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_sec_cuat_up_ma_zs": { "title": "Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed upper secondary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CUAT.UP.MA.ZS (https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_sec_cuat_up_zs": { "title": "Educational attainment, at least completed upper secondary, population 25+, total (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed upper secondary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed upper secondary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.CUAT.UP.ZS (https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.CUAT.UP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_sec_durs": { "title": "Secondary education, duration (years)", "description_from_producer": "Secondary duration refers to the number of grades (years) in secondary school.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Data on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the number of years that a country's laws or regulations specify for secondary education. It aids in identifying the population of school-aged children at different educational levels. Additionally, this metric serves as crucial input data necessary for generating various indicators and evaluating a country's capacity to meet educational demand.\n\n### Development relevance:\nSecondary education equips individuals with vital skills that are essential for active participation in society. Sustainable Development Goal (SDG) target 4.1 promotes completion of secondary education for all young people by endorsing free, equitable, and high-quality education at this level.\n\n### Limitations and exceptions:\nThe presence of national legislation does not guarantee that countries will implement it effectively, nor does it ensure that parents will take advantage of the provisions available for their children.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.DURS (https://data.worldbank.org/indicator/SE.SEC.DURS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.DURS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "years", "short_unit": "years", "display": { "shortUnit": "years", "unit": "years" } }, "se_sec_enrl": { "title": "Secondary education, pupils", "description_from_producer": "Secondary education pupils is the total number of pupils enrolled at secondary level in public and private schools.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The International Standard Classification of Education (ISCED) differentiates between lower secondary education and upper secondary education within its educational programs. ISCED level 2, or lower secondary education, are designed to build upon the foundational literacy and numeracy skills acquired at ISCED level 1. The objective at this stage is to establish a base for lifelong learning and human development, which can be further enhanced by subsequent educational opportunities. Certain education systems may introduce vocational education programs at this level to equip individuals with skills that are directly applicable to the workforce.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nAt ISCED level 3, or upper secondary education, programs are structured to finalize the secondary education phase, preparing students for higher education or to enter the job market with relevant skills, or in some cases, to achieve both objectives.\n\n### Development relevance:\nSecondary education provides children with essential skills that enable their full participation in society. Sustainable Development Goal target 4.1 calls for all children to complete free, equitable, and high-quality primary and secondary education, with the goal of attaining significant and effective learning outcomes by 2030.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.ENRL (https://data.worldbank.org/indicator/SE.SEC.ENRL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_sec_enrl_fe_zs": { "title": "Secondary education, pupils (% female)", "description_from_producer": "Female pupils as a percentage of total pupils at secondary level includes enrollments in public and private schools.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nThe share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education.\n\n### Limitations and exceptions:\nThe percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.ENRL.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.ENRL.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRL.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% female", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% female" } }, "se_sec_enrl_gc": { "title": "Secondary education, general pupils", "description_from_producer": "Secondary general pupils are the number of secondary students enrolled in general education programs, including teacher training.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.ENRL.GC (https://data.worldbank.org/indicator/SE.SEC.ENRL.GC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRL.GC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_sec_enrl_gc_fe_zs": { "title": "Secondary education, general pupils (% female)", "description_from_producer": "Secondary general pupils are the number of secondary students enrolled in general education programs, including teacher training.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nThe share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education.\n\n### Limitations and exceptions:\nThe percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.ENRL.GC.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.ENRL.GC.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRL.GC.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% female", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% female" } }, "se_sec_enrl_lo_tc_zs": { "title": "Pupil-teacher ratio, lower secondary", "description_from_producer": "Lower secondary school pupil-teacher ratio is the average number of pupils per teacher in lower secondary school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education.\n\n### Limitations and exceptions:\nThe comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.ENRL.LO.TC.ZS (https://data.worldbank.org/indicator/SE.SEC.ENRL.LO.TC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRL.LO.TC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_sec_enrl_tc_zs": { "title": "Pupil-teacher ratio, secondary", "description_from_producer": "Secondary school pupil-teacher ratio is the average number of pupils per teacher in secondary school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education.\n\n### Limitations and exceptions:\nThe comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.ENRL.TC.ZS (https://data.worldbank.org/indicator/SE.SEC.ENRL.TC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRL.TC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_sec_enrl_up_tc_zs": { "title": "Pupil-teacher ratio, upper secondary", "description_from_producer": "Upper secondary school pupil-teacher ratio is the average number of pupils per teacher in upper secondary school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education.\n\n### Limitations and exceptions:\nThe comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.ENRL.UP.TC.ZS (https://data.worldbank.org/indicator/SE.SEC.ENRL.UP.TC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRL.UP.TC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_sec_enrl_vo": { "title": "Secondary education, vocational pupils", "description_from_producer": "Secondary vocational pupils are the number of secondary students enrolled in technical and vocational education programs, including teacher training.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Enrollment includes Individuals officially registered in a given educational programme, or stage or module thereof, regardless of age.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.ENRL.VO (https://data.worldbank.org/indicator/SE.SEC.ENRL.VO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRL.VO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_sec_enrl_vo_fe_zs": { "title": "Secondary education, vocational pupils (% female)", "description_from_producer": "Secondary vocational pupils are the number of secondary students enrolled in technical and vocational education programs, including teacher training.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Percentage of female enrollment is calculated by dividing the total number of female students at a given level of education by the total enrollment at the same level, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\n\n### Development relevance:\nThe share of girls allows an assessment on gender composition in school enrollment. A value greater than 50% indicates participation of more girls at a specific level or programme of education.\n\n### Limitations and exceptions:\nThe percentage of female enrollment is limited in assessing gender parity, because it's affected by the gender composition of population. Ratio of female to male in enrollment rate provides a population adjusted measure of gender parity.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.ENRL.VO.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.ENRL.VO.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRL.VO.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% female", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% female" } }, "se_sec_enrr": { "title": "School enrollment, secondary (% gross)", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system.\n\n### Development relevance:\nSecondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.ENRR (https://data.worldbank.org/indicator/SE.SEC.ENRR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% gross", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% gross" } }, "se_sec_enrr_fe": { "title": "School enrollment, secondary, female (% gross)", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system.\n\n### Development relevance:\nSecondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.ENRR.FE (https://data.worldbank.org/indicator/SE.SEC.ENRR.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRR.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% gross", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% gross" } }, "se_sec_enrr_ma": { "title": "School enrollment, secondary, male (% gross)", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for secondary school is calculated by dividing the number of students enrolled in secondary education regardless of age by the population of the age group which officially corresponds to secondary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system.\n\n### Development relevance:\nSecondary education acts as a critical intermediary that not only builds upon the foundational knowledge acquired in primary education but also equips students for various pathways, including immediate entry into the workforce, further education in postsecondary non-tertiary institutions, or advancement to higher education. This indicator assesses the aggregate participation rate in secondary education, reflecting the education system's capacity to enroll students within a designated age group.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.ENRR.MA (https://data.worldbank.org/indicator/SE.SEC.ENRR.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.ENRR.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% gross", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% gross" } }, "se_sec_nenr": { "title": "School enrollment, secondary (% net)", "description_from_producer": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nGross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.NENR (https://data.worldbank.org/indicator/SE.SEC.NENR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.NENR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% net", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% net" } }, "se_sec_nenr_fe": { "title": "School enrollment, secondary, female (% net)", "description_from_producer": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nGross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.NENR.FE (https://data.worldbank.org/indicator/SE.SEC.NENR.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.NENR.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% net", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% net" } }, "se_sec_nenr_ma": { "title": "School enrollment, secondary, male (% net)", "description_from_producer": "Net enrollment rate is the ratio of children of official school age who are enrolled in school to the population of the corresponding official school age. Secondary education completes the provision of basic education that began at the primary level, and aims at laying the foundations for lifelong learning and human development, by offering more subject- or skill-oriented instruction using more specialized teachers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Net enrollment rate for secondary school is calculated by dividing the number of students of official school age enrolled in secondary education by the population of the age group which officially corresponds to secondary education, and multiplying by 100.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nGross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system. The net enrollment rate excludes overage and underage students and more accurately captures the system's coverage and internal efficiency. Differences between the gross enrollment ratio and the net enrollment rate show the incidence of overage and underage enrollments.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.NENR.MA (https://data.worldbank.org/indicator/SE.SEC.NENR.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.NENR.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% net", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% net" } }, "se_sec_priv_zs": { "title": "School enrollment, secondary, private (% of total secondary)", "description_from_producer": "Private enrollment refers to pupils or students enrolled in institutions that are not operated by a public authority but controlled and managed, whether for profit or not, by a private body such as a nongovernmental organization, religious body, special interest group, foundation or business enterprise.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The share of students in private secondary school is calculated by dividing the number of students enrolled in private educational institutions at secondary level by total enrollment (public and private) at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of enrollment in private institutions indicates the scale and capacity of private education within a country. A high percentage suggests strong involvement of the non-governmental sector (including religious bodies, other organizations, associations, communities, private enterprises or persons) in providing organized educational programmes. However, in countries where private institutions are substantially subsidized or aided by the government, the distinction between private and public educational institutions may be less clear-cut especially when certain students are directly financed through government scholarships.\n\n### Development relevance:\nThe indicator reflects the proportion of students attending private educational institutions. Globally, private schools educate approximately 350 million children, and there has been a notable rise in the prevalence of private institutions (https://world-education-blog.org/2021/12/10/new-2021-2-gem-report-out-today-who-chooses-who-loses/). UNESCO emphasizes the importance of comprehensively understanding the contexts and frameworks within which both public and private schools function in each nation. This understanding is vital to guarantee that children's right to education is upheld and that their specific educational requirements are addressed.\n\n### Limitations and exceptions:\nReligious or private schools, which are not registered with the government or don't follow the common national curriculum, may not be captured.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.PRIV.ZS (https://data.worldbank.org/indicator/SE.SEC.PRIV.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.PRIV.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total secondary", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total secondary" } }, "se_sec_prog_fe_zs": { "title": "Progression to secondary school, female (%)", "description_from_producer": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100.\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity.\n\n### Limitations and exceptions:\nThe quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.PROG.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.PROG.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.PROG.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "se_sec_prog_ma_zs": { "title": "Progression to secondary school, male (%)", "description_from_producer": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100.\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity.\n\n### Limitations and exceptions:\nThe quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.PROG.MA.ZS (https://data.worldbank.org/indicator/SE.SEC.PROG.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.PROG.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "se_sec_prog_zs": { "title": "Progression to secondary school (%)", "description_from_producer": "Progression to secondary school refers to the number of new entrants to the first grade of secondary school in a given year as a percentage of the number of students enrolled in the final grade of primary school in the previous year (minus the number of repeaters from the last grade of primary education in the given year).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Effective transition rate is calculated by dividing the number of new entrants in the first grade of secondary education in a given year (t) by the number of students who enrolled in the final grade of primary education in the previous school year (t-1) minus the number of repeaters from the last grade of primary education in the given year (t), and multiplying by 100.\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe effective transition rate from primary to secondary education conveys the degree of access or transition between the two levels. As completing primary education is a prerequisite for participating in lower secondary education, growing numbers of primary completers will inevitably create pressure for more available places at the secondary level. A low effective transition rate can signal such problems as an inadequate examination and promotion system or insufficient secondary education capacity.\n\n### Limitations and exceptions:\nThe quality of data on the transition rate is affected when new entrants and repeaters are not correctly distinguished. Students who interrupt their studies after completing primary education could also affect data quality.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.SEC.PROG.ZS (https://data.worldbank.org/indicator/SE.SEC.PROG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.PROG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "se_sec_tcaq_fe_zs": { "title": "Trained teachers in secondary education, female (% of female teachers)", "description_from_producer": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCAQ.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.TCAQ.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCAQ.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female teachers", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female teachers" } }, "se_sec_tcaq_lo_fe_zs": { "title": "Trained teachers in lower secondary education, female (% of female teachers)", "description_from_producer": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCAQ.LO.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female teachers", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female teachers" } }, "se_sec_tcaq_lo_ma_zs": { "title": "Trained teachers in lower secondary education, male (% of male teachers)", "description_from_producer": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCAQ.LO.MA.ZS (https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male teachers", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male teachers" } }, "se_sec_tcaq_lo_zs": { "title": "Trained teachers in lower secondary education (% of total teachers)", "description_short": "Share of teachers in [lower secondary education](#dod:lower-secondary-education) who have received at least the minimum organized pedagogical training required for teaching at that level, shown as a percentage of all teachers.", "description_from_producer": "Trained teachers in lower secondary education are the percentage of lower secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "description_key": "- Not all teachers receive the same level of preparation before entering the classroom. This indicator measures the percentage of teachers at each education level who have completed at least the minimum organized pedagogical training required for teaching at that level in their country.\n- It captures the share of teachers who are pedagogically trained, expressed as a percentage of all teachers at that level. For example, if there are 1,000 primary teachers and 850 have completed the required training, the indicator shows 85%.\n- The \"minimum training\" refers to whatever organized pedagogical preparation each country requires \u2014 pre-service teacher education programs, in-service training courses, or certification requirements that focus on teaching methods rather than subject knowledge.\n- Values below 100% suggest some teachers are working without the pedagogical training their own country considers necessary. However, cross-country comparisons can be misleading since training requirements vary significantly between nations.\n- This indicator does not account for differences in teachers' experience, professional status, teaching methods, materials, or classroom conditions \u2014 all of which affect the quality of teaching and learning. Some teachers without formal training may also acquire equivalent skills through professional experience.\n- The reference years reflect the school year. In countries where the school year spans two calendar years (e.g. September 2010 to June 2011), the reference year is the year in which the school year ended (2011 in this example).\n- The data comes from administrative records collected at schools and other organized learning centers, typically including information on teacher academic credentials and employment status.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCAQ.LO.ZS (https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCAQ.LO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of teachers", "short_unit": "%", "display": { "numDecimalPlaces": 1, "tolerance": 5, "name": "Lower secondary", "shortUnit": "%", "unit": "% of teachers" }, "presentation": { "grapher_config": { "maxTime": 2024, "hasMapTab": true, "map": { "time": 2024, "timeTolerance": 10, "colorScale": { "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 ], "baseColorScheme": "Blues" } } }, "title_public": "Share of trained teachers in lower secondary education", "topic_tags": [ "Global Education" ] } }, "se_sec_tcaq_ma_zs": { "title": "Trained teachers in secondary education, male (% of male teachers)", "description_from_producer": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCAQ.MA.ZS (https://data.worldbank.org/indicator/SE.SEC.TCAQ.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCAQ.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male teachers", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male teachers" } }, "se_sec_tcaq_up_fe_zs": { "title": "Trained teachers in upper secondary education, female (% of female teachers)", "description_from_producer": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe percentage of trained educators serves as a barometer for a country's commitment to improving its teaching workforce, and increasing this percentage aligns with the aims of Sustainable Development Goal target 4.c. Female educators, in particular, are vital as they provide inspiration and encouragement for young girls to continue their education. These professionals are instrumental in engaging and maintaining girls' attendance in schools, confronting entrenched gender biases in communities, raising parental aspirations for their daughters, and aiding in the reduction of the educational attainment disparity between male and female students.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nNonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCAQ.UP.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female teachers", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female teachers" } }, "se_sec_tcaq_up_ma_zs": { "title": "Trained teachers in upper secondary education, male (% of male teachers)", "description_from_producer": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCAQ.UP.MA.ZS (https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male teachers", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male teachers" } }, "se_sec_tcaq_up_zs": { "title": "Trained teachers in upper secondary education (% of total teachers)", "description_short": "Share of teachers in [upper secondary education](#dod:upper-secondary-education) who have received at least the minimum organized pedagogical training required for teaching at that level, shown as a percentage of all teachers.", "description_from_producer": "Trained teachers in upper secondary education are the percentage of upper secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "description_key": "- Not all teachers receive the same level of preparation before entering the classroom. This indicator measures the percentage of teachers at each education level who have completed at least the minimum organized pedagogical training required for teaching at that level in their country.\n- It captures the share of teachers who are pedagogically trained, expressed as a percentage of all teachers at that level. For example, if there are 1,000 primary teachers and 850 have completed the required training, the indicator shows 85%.\n- The \"minimum training\" refers to whatever organized pedagogical preparation each country requires \u2014 pre-service teacher education programs, in-service training courses, or certification requirements that focus on teaching methods rather than subject knowledge.\n- Values below 100% suggest some teachers are working without the pedagogical training their own country considers necessary. However, cross-country comparisons can be misleading since training requirements vary significantly between nations.\n- This indicator does not account for differences in teachers' experience, professional status, teaching methods, materials, or classroom conditions \u2014 all of which affect the quality of teaching and learning. Some teachers without formal training may also acquire equivalent skills through professional experience.\n- The reference years reflect the school year. In countries where the school year spans two calendar years (e.g. September 2010 to June 2011), the reference year is the year in which the school year ended (2011 in this example).\n- The data comes from administrative records collected at schools and other organized learning centers, typically including information on teacher academic credentials and employment status.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCAQ.UP.ZS (https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCAQ.UP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of teachers", "short_unit": "%", "display": { "numDecimalPlaces": 1, "tolerance": 5, "name": "Upper secondary", "shortUnit": "%", "unit": "% of teachers" }, "presentation": { "grapher_config": { "maxTime": 2024, "hasMapTab": true, "map": { "time": 2024, "timeTolerance": 10, "colorScale": { "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 ], "baseColorScheme": "Blues" } } }, "title_public": "Share of trained teachers in upper secondary education", "topic_tags": [ "Global Education" ] } }, "se_sec_tcaq_zs": { "title": "Trained teachers in secondary education (% of total teachers)", "description_from_producer": "Trained teachers in secondary education are the percentage of secondary school teachers who have received the minimum organized teacher training (pre-service or in-service) required for teaching in a given country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Share of trained teachers is calculated by dividing the number of trained teachers of the specified level of education by total number of teachers at the same level of education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator quantifies the proportion of educators within the teaching workforce who have received comprehensive pedagogical training. A high percentage signifies that a substantial majority of students are instructed by teachers equipped with the necessary pedagogical skills and training to teach effectively. The role of teachers is pivotal in upholding the standard of education delivered. Optimal educational outcomes are achieved when all teachers are provided with sufficient, pertinent, and targeted pedagogical training, enabling them to teach at their designated educational level. Moreover, it is crucial that teachers possess the academic qualifications required to competently cover the subject matter they are tasked with teaching.\n\n### Development relevance:\nThe proportion of trained educators is indicative of a nation's dedication to the enhancement of its educational workforce, and the expansion of this metric is a specific objective under Sustainable Development Goal target 4.c. Teachers serve as a crucial asset, particularly for first-generation learners within their families, who depend significantly on educators to gain fundamental literacy skills. Nonetheless, a surge in student enrollment can lead to a scarcity of qualified teachers. The allocation of funds for education plays a vital role in ensuring the proper distribution of teachers, as their salaries constitute a significant portion of educational expenditures. This scarcity of trained educators can lead to the employment of less qualified teachers in more disadvantaged regions.\n\n### Limitations and exceptions:\nThis indicator does not take into account differences in teachers' experiences and status, teaching methods, teaching materials, and classroom conditions - all factors that affect the quality of teaching and learning. Some teachers without formal training may have acquired equivalent pedagogical skills through professional experience. In addition, national standards regarding teacher qualifications and pedagogical skills may vary.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file, date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCAQ.ZS (https://data.worldbank.org/indicator/SE.SEC.TCAQ.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCAQ.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total teachers", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total teachers" } }, "se_sec_tchr": { "title": "Secondary education, teachers", "description_from_producer": "Secondary education, teachers refers to the total number of teachers at secondary level, including full-time and part-time teachers.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Teachers refer to persons employed full-time or part-time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) or who work occasionally or in a voluntary capacity in educational institutions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The metric of personnel count primarily engaged in teaching and/or research reflects the scale, diversity, and distribution of educational staff within a nation's academic institutions. An increased count is anticipated to enrich the educational setting by means of dedicated teaching, practical research, academic pursuits, and contributions to the service of national educational policies.\n\n### Development relevance:\nTeachers are pivotal in molding the future and unleashing the potential of each student. The United Nations Educational, Scientific and Cultural Organization (UNESCO) highlights a global teacher shortage as a significant obstacle in realizing Sustainable Development Goal 4, which aims for inclusive and equitable quality education.\n\n### Limitations and exceptions:\nThe precision of this indicator can be influenced by the enumeration method used, such as headcount or 'full-time equivalent' count of teachers.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCHR (https://data.worldbank.org/indicator/SE.SEC.TCHR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCHR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_sec_tchr_fe": { "title": "Secondary education, teachers, female", "description_from_producer": "Secondary education, teachers, female, refers to the total number of female teachers at secondary level, including full-time and part-time teachers.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Teachers refer to persons employed full-time or part-time in an official capacity to guide and direct the learning experience of pupils and students, irrespective of their qualifications or the delivery mechanism, i.e. face-to-face and/or at a distance. This definition excludes educational personnel who have no active teaching duties (e.g. headmasters, headmistresses or principals who do not teach) or who work occasionally or in a voluntary capacity in educational institutions.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The metric of personnel count primarily engaged in teaching and/or research reflects the scale, diversity, and distribution of educational staff within a nation's academic institutions. An increased count is anticipated to enrich the educational setting by means of dedicated teaching, practical research, academic pursuits, and contributions to the service of national educational policies.\n\n### Development relevance:\nAlthough there have been advancements, girls in low-income countries continue to face significant barriers to accessing secondary education. The presence of female teachers is crucial in this context, as they act as role models, inspiring and motivating girls to pursue their education. These educators play a pivotal role in attracting and retaining girls in schools, challenging deep-seated gender stereotypes within communities, elevating parental expectations for their daughters, and contributing to the narrowing of the educational achievement gap between boys and girls.\n\n### Limitations and exceptions:\nThe indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCHR.FE (https://data.worldbank.org/indicator/SE.SEC.TCHR.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCHR.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_sec_tchr_fe_zs": { "title": "Secondary education, teachers (% female)", "description_from_producer": "Female teachers as a percentage of total secondary education teachers includes full-time and part-time teachers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The share of female teachers in secondary education is calculated by dividing the total number of female teachers at secondary level of education by the total number of teachers at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities.\n\n### Development relevance:\nAlthough there have been advancements, girls in low-income countries continue to face significant barriers to accessing secondary education. The presence of female teachers is crucial in this context, as they act as role models, inspiring and motivating girls to pursue their education. These educators play a pivotal role in attracting and retaining girls in schools, challenging deep-seated gender stereotypes within communities, elevating parental expectations for their daughters, and contributing to the narrowing of the educational achievement gap between boys and girls.\n\n### Limitations and exceptions:\nThe indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.TCHR.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.TCHR.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.TCHR.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% female", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% female" } }, "se_sec_uner_lo_fe_zs": { "title": "Adolescents out of school, female (% of female lower secondary school age)", "description_from_producer": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level.\n\n### Development relevance:\nEnsuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility.\n\n### Limitations and exceptions:\nThe administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.UNER.LO.FE.ZS (https://data.worldbank.org/indicator/SE.SEC.UNER.LO.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.UNER.LO.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female lower secondary school age", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female lower secondary school age" } }, "se_sec_uner_lo_ma_zs": { "title": "Adolescents out of school, male (% of male lower secondary school age)", "description_from_producer": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level.\n\n### Development relevance:\nEnsuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility.\n\n### Limitations and exceptions:\nThe administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.UNER.LO.MA.ZS (https://data.worldbank.org/indicator/SE.SEC.UNER.LO.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.UNER.LO.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male lower secondary school age", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male lower secondary school age" } }, "se_sec_uner_lo_zs": { "title": "Adolescents out of school (% of lower secondary school age)", "description_from_producer": "Adolescents out of school are the percentage of lower secondary school age adolescents who are not enrolled in school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The rate of out-of-school adolescents allows to compare across countries with different population sizes. It shows the share of official lower secondary age adolescents who never attended school or dropped out to the population of official lower secondary school age.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses. The reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): This indicator measures the proportion of the population within the official age range for a specific educational level who are not currently enrolled in school. The data aids in identifying these individuals to tailor targeted interventions and formulate effective policies, ensuring that all children have the opportunity to attend school. It encompasses children who have never attended school, those who may enroll at a later age, and those who have started education but did not continue to the expected age of completion for the specified level.\n\n### Development relevance:\nEnsuring access to education is fundamental to social and economic advancement, aligning with the global development agenda. The out-of-school rate serves as a critical metric within the Sustainable Development Goal 4 framework, providing a measure to evaluate progress toward achieving universal education. This rate, alongside the number of out-of-school children and adolescents, highlights the urgency to enhance educational accessibility.\n\n### Limitations and exceptions:\nThe administrative data used in the calculation of the rate of out-of-school children are based on enrolment at a specific date which can bias the results by either counting enrolled children who never attend school or by omitting those who enroll after the reference date for reporting enrolment data. Furthermore, children who drop out of school after the reference date are not counted as out of school. Discrepancies between enrolment and population data from different sources can also result in over- or underestimates of the rate. Lastly, the international comparability of this indicator can be affected by the use of different concepts of enrolment and out-of-school children across countries.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.SEC.UNER.LO.ZS (https://data.worldbank.org/indicator/SE.SEC.UNER.LO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.SEC.UNER.LO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of lower secondary school age", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of lower secondary school age" } }, "se_ter_cuat_ba_fe_zs": { "title": "Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relatively high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.BA.FE.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.BA.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.BA.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_cuat_ba_ma_zs": { "title": "Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.BA.MA.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.BA.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.BA.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_cuat_ba_zs": { "title": "Educational attainment, at least Bachelor's or equivalent, population 25+, total (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed Bachelor's or equivalent.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Bachelor's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.BA.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.BA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.BA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_cuat_do_fe_zs": { "title": "Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relatively high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.DO.FE.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.DO.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.DO.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_cuat_do_ma_zs": { "title": "Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relatively high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.DO.MA.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.DO.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.DO.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_cuat_do_zs": { "title": "Educational attainment, Doctoral or equivalent, population 25+, total (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed Doctoral or equivalent.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Doctoral or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.DO.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.DO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.DO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_cuat_ms_fe_zs": { "title": "Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed Master's or equivalent.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.MS.FE.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.MS.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.MS.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_cuat_ms_ma_zs": { "title": "Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed Master's or equivalent.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.MS.MA.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.MS.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.MS.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_cuat_ms_zs": { "title": "Educational attainment, at least Master's or equivalent, population 25+, total (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed Master's or equivalent.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed Master's or equivalent by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.MS.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.MS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.MS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_cuat_st_fe_zs": { "title": "Educational attainment, at least completed short-cycle tertiary, population 25+, female (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relative high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and do not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.ST.FE.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.ST.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.ST.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_cuat_st_ma_zs": { "title": "Educational attainment, at least completed short-cycle tertiary, population 25+, male (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relatively high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.ST.MA.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.ST.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.ST.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_cuat_st_zs": { "title": "Educational attainment, at least completed short-cycle tertiary, population 25+, total (%) (cumulative)", "description_from_producer": "The percentage of population ages 25 and over that attained or completed short-cycle tertiary education.\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of population ages 25 and older who attained or completed short-cycle tertiary education by the total population of the same age group and multiplying by 100. The number 0 means zero or small enough that the number would round to zero.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData are collected by the UNESCO Institute for Statistics mainly from national population census, household survey, and labour force survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\nStatistical concept(s): Educational attainment indicators by education level, serve as a measure of the accumulated stock of knowledge, skills, and competencies that are typically associated with the completion of each level. These indicators not only shed light on the disparities in educational achievement among various demographic groups, thereby offering insights into the present and past efficacy of the education system in ensuring equitable access to education, but they also mirror the structure and performance of the education system itself. Furthermore, such indicators are instrumental in guiding policy decisions aimed at expanding educational opportunities.\n\n### Development relevance:\nA relatively high concentration of the adult population in a given level of education reflects the capacity of the educational system in the corresponding level of education. Educational attainment is closely related to the skills and competencies of a country's population, and could be seen as a proxy of both the quantitative and qualitative aspects of the stock of human capital.\n\n### Limitations and exceptions:\nCaution is required when using this indicator for cross-country comparison, since the countries do not always classify degrees and qualifications at the same International Standard Classification of Education (ISCED) levels, even if they are received at roughly the same age or after a similar number of years of schooling. Also, certain educational programmes and study courses cannot be easily classified according to ISCED. This indicator only measures educational attainment in terms of level of education attained, i.e. years of schooling, and does not necessarily reveal the quality of the education (learning achievement and other impacts).", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.CUAT.ST.ZS (https://data.worldbank.org/indicator/SE.TER.CUAT.ST.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.CUAT.ST.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "cumulative", "display": { "unit": "cumulative" } }, "se_ter_enrl_tc_zs": { "title": "Pupil-teacher ratio, tertiary", "description_from_producer": "Tertiary school pupil-teacher ratio is the average number of pupils per teacher in tertiary school.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Pupil-teacher ratio is calculated by dividing the number of students at the specified level of education by the number of teachers at the same level of education.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe pupil-teacher ratio is often used to compare the quality of schooling across countries, but it is often weakly related to student learning and quality of education.\n\n### Limitations and exceptions:\nThe comparability of pupil-teacher ratios across countries is affected by the definition of teachers and by differences in class size by grade and in the number of hours taught, as well as the different practices countries employ such as part-time teachers, school shifts, and multi-grade classes. Moreover, the underlying enrollment levels are subject to a variety of reporting errors.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.TER.ENRL.TC.ZS (https://data.worldbank.org/indicator/SE.TER.ENRL.TC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.ENRL.TC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "se_ter_enrr": { "title": "School enrollment, tertiary (% gross)", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system.\n\n### Development relevance:\nSustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.ENRR (https://data.worldbank.org/indicator/SE.TER.ENRR). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.ENRR", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% gross", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% gross" } }, "se_ter_enrr_fe": { "title": "School enrollment, tertiary, female (% gross)", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system.\n\n### Development relevance:\nSustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.ENRR.FE (https://data.worldbank.org/indicator/SE.TER.ENRR.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.ENRR.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% gross", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% gross" } }, "se_ter_enrr_ma": { "title": "School enrollment, tertiary, male (% gross)", "description_from_producer": "Gross enrollment ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown. Tertiary education, whether or not to an advanced research qualification, normally requires, as a minimum condition of admission, the successful completion of education at the secondary level.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Gross enrollment ratio for tertiary school is calculated by dividing the number of students enrolled in tertiary education regardless of age by the population of the age group which officially corresponds to tertiary education, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Population data are drawn from the United Nations Population Division. Using a single source for population data standardizes definitions, estimations, and interpolation methods, ensuring a consistent methodology across countries and minimizing potential enumeration problems in national censuses.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): Gross enrollment ratios indicate the capacity of each level of the education system, but a high ratio may reflect a substantial number of overage children enrolled in each grade because of repetition or late entry rather than a successful education system.\n\n### Development relevance:\nSustainable Development Goal (SDG) target 4.3 is committed to providing equitable access to affordable and high-quality technical, vocational, and tertiary education, including university, for both women and men. This particular indicator reflects the overall capacity of the educational infrastructure to support enrolment within a specified age demographic at the tertiary level.\n\n### Limitations and exceptions:\nEnrollment indicators are based on annual school surveys, but do not necessarily reflect actual attendance or dropout rates during the year. Also, the length of education differs across countries and can influence enrollment rates, although the International Standard Classification of Education (ISCED) tries to minimize the difference. For example, a shorter duration for primary education tends to increase the rate; a longer one to decrease it (in part because older children are more at risk of dropping out). Moreover, age at enrollment may be inaccurately estimated or misstated, especially in communities where registration of births is not strictly enforced.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS) Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), type: Bulk file (csv), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.ENRR.MA (https://data.worldbank.org/indicator/SE.TER.ENRR.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.ENRR.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% gross", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% gross" } }, "se_ter_tchr_fe_zs": { "title": "Tertiary education, academic staff (% female)", "description_from_producer": "Tertiary education, academic staff (% female) is the share of female academic staff in tertiary education.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The share of female academic staff in tertiary education is calculated by dividing the total number of female academic staff at tertiary level of education by the total number of academic staff at the same level, and multiplying by 100.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s): The share of female teachers shows the level of gender representation in the teaching force. A value of greater than 50% indicates more opportunities or preference for women to participate in teaching activities.\n\n### Development relevance:\nThis indicator reflects the gender distribution within the teaching profession. It serves as a tool for evaluating the necessity of creating opportunities and incentives to promote female participation in educational instruction at various levels. According to UNESCO, there is a global trend of women being disproportionately represented in the teaching workforce. Nonetheless, this representation declines at the tertiary education level, where men are more prevalent, and women are less likely to attain senior and leadership roles within higher education institutions.\n\n### Limitations and exceptions:\nThe indicator measures the level of gender representation in the teaching profession, rather than the effectiveness and quality of teaching.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.TER.TCHR.FE.ZS (https://data.worldbank.org/indicator/SE.TER.TCHR.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.TER.TCHR.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% female", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% female" } }, "se_xpd_cprm_zs": { "title": "Current education expenditure, primary (% of total expenditure in primary public institutions)", "description_from_producer": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration).\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Current expenditure, primary is calculated by dividing all current expenditure in public institutions of primary education by total expenditure (current and capital) in public institutions of primary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material.\n\n### Development relevance:\nInvesting in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages.\n\n### Limitations and exceptions:\nThe indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.XPD.CPRM.ZS (https://data.worldbank.org/indicator/SE.XPD.CPRM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.CPRM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total expenditure in primary public institutions", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total expenditure in primary public institutions" } }, "se_xpd_csec_zs": { "title": "Current education expenditure, secondary (% of total expenditure in secondary public institutions)", "description_from_producer": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration).\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Current expenditure, secondary is calculated by dividing all current expenditure in public institutions of secondary education by total expenditure (current and capital) in public institutions of secondary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material.\n\n### Development relevance:\nInvesting in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages.\n\n### Limitations and exceptions:\nThe indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.XPD.CSEC.ZS (https://data.worldbank.org/indicator/SE.XPD.CSEC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.CSEC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total expenditure in secondary public institutions", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total expenditure in secondary public institutions" } }, "se_xpd_cter_zs": { "title": "Current education expenditure, tertiary (% of total expenditure in tertiary public institutions)", "description_from_producer": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration).\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Current expenditure, tertiary is calculated by dividing all current expenditure in public institutions of tertiary education by total expenditure (current and capital) in public institutions of tertiary education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material.\n\n### Development relevance:\nInvesting in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages.\n\n### Limitations and exceptions:\nThe indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.XPD.CTER.ZS (https://data.worldbank.org/indicator/SE.XPD.CTER.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.CTER.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total expenditure in tertiary public institutions", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total expenditure in tertiary public institutions" } }, "se_xpd_ctot_zs": { "title": "Current education expenditure, total (% of total expenditure in public institutions)", "description_from_producer": "Current expenditure is expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Current expenditure is consumed within the current year and would have to be renewed if needed in the following year. It includes staff compensation and current expenditure other than for staff compensation (ex. on teaching materials, ancillary services and administration).\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Current expenditure, total is calculated by dividing all current expenditure in public institutions of all levels of education by total expenditure (current and capital) in public institutions of all levels of education, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data on education spending is obtained from national governments through their responses to the annual UIS survey on formal education or the UNESCO-OECD-Eurostat (UOE) data collection initiative. The figures reported in the education expenditure questionnaire are usually derived from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office.\nStatistical concept(s): Educational expenditure in public institutions includes all staff compensation, teaching staff/non teaching staff compensation, current expenditure other than staff compensation, expenditure on school books and teaching material.\n\n### Development relevance:\nInvesting in education is a catalyst for economic expansion, productivity improvement, and the advancement of both individual and societal well-being. It also serves as a mechanism to mitigate social disparities. The value of this indicator highlights the focus of governmental policies, as evidenced by the distribution of spending by expenditure type and the nature of expenditures throughout various educational stages.\n\n### Limitations and exceptions:\nThe indicator depends on comprehensive and accurate data regarding government expenditure by type and nature of spending. In some cases, data on total government expenditure on education only includes the Ministry of Education, excluding other ministries that may also allocate part of their budget to educational services.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.XPD.CTOT.ZS (https://data.worldbank.org/indicator/SE.XPD.CTOT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.CTOT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total expenditure in public institutions", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total expenditure in public institutions" } }, "se_xpd_prim_pc_zs": { "title": "Government expenditure per student, primary (% of GDP per capita)", "description_from_producer": "Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: General government expenditure per student in primary education is calculated by dividing total government expenditure on primary education by the number of students at primary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.XPD.PRIM.PC.ZS (https://data.worldbank.org/indicator/SE.XPD.PRIM.PC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.PRIM.PC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP per capita", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP per capita" } }, "se_xpd_prim_zs": { "title": "Expenditure on primary education (% of government expenditure on education)", "description_from_producer": "Expenditure on primary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: The share of expenditure on primary education to total government expenditure on education is calculated by dividing government expenditure on primary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe share of government expenditure for a specific education level allows an assessment of the priority a government assigns to a level of education relative to other levels. Enrolment and the relative costs per student between different levels of education should be also taken into account.\n\n### Limitations and exceptions:\nData disaggregated by level of education are estimates in some instances. It is often difficult to separate lower from upper secondary education expenditure, or pre-primary from primary.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.XPD.PRIM.ZS (https://data.worldbank.org/indicator/SE.XPD.PRIM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.PRIM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of government expenditure on education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of government expenditure on education" } }, "se_xpd_seco_pc_zs": { "title": "Government expenditure per student, secondary (% of GDP per capita)", "description_from_producer": "Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: General government expenditure per student in secondary education is calculated by dividing total government expenditure on secondary education by the number of students at secondary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.XPD.SECO.PC.ZS (https://data.worldbank.org/indicator/SE.XPD.SECO.PC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.SECO.PC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP per capita", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP per capita" } }, "se_xpd_seco_zs": { "title": "Expenditure on secondary education (% of government expenditure on education)", "description_from_producer": "Expenditure on secondary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: The share of expenditure on secondary education to total government expenditure on education is calculated by dividing government expenditure on secondary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe share of government expenditure for a specific education level allows an assessment of the priority a government assigns to a level of education relative to other levels. Enrolment and the relative costs per student between different levels of education should be also taken into account.\n\n### Limitations and exceptions:\nData disaggregated by level of education are estimates in some instances. It is often difficult to separate lower from upper secondary education expenditure, or pre-primary from primary.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.XPD.SECO.ZS (https://data.worldbank.org/indicator/SE.XPD.SECO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.SECO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of government expenditure on education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of government expenditure on education" } }, "se_xpd_tert_pc_zs": { "title": "Government expenditure per student, tertiary (% of GDP per capita)", "description_from_producer": "Government expenditure per student is the average general government expenditure (current, capital, and transfers) per student in the given level of education, expressed as a percentage of GDP per capita.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: General government expenditure per student in tertiary education is calculated by dividing total government expenditure on tertiary education by the number of students at tertiary level, expressed as a percentage of GDP per capita. Aggregate data are World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011. Data on GDP per capita come from the World Bank.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.XPD.TERT.PC.ZS (https://data.worldbank.org/indicator/SE.XPD.TERT.PC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.TERT.PC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP per capita", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP per capita" } }, "se_xpd_tert_zs": { "title": "Expenditure on tertiary education (% of government expenditure on education)", "description_from_producer": "Expenditure on tertiary education is expressed as a percentage of total general government expenditure on education. General government usually refers to local, regional and central governments.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: The share of expenditure on tertiary education to total government expenditure on education is calculated by dividing government expenditure on tertiary education by total government expenditure on education (all levels combined), and multiplying by 100. Aggregate data are based on World Bank estimates.\n\nData on education are collected by the UNESCO Institute for Statistics from official responses to its annual education survey. All the data are mapped to the International Standard Classification of Education (ISCED) to ensure the comparability of education programs at the international level. The current version was formally adopted by UNESCO Member States in 2011.\n\nThe reference years reflect the school year for which the data are presented. In some countries the school year spans two calendar years (for example, from September 2010 to June 2011); in these cases the reference year refers to the year in which the school year ended (2011 in the example).\nStatistical concept(s):\n\n### Development relevance:\nThe share of government expenditure for a specific education level allows an assessment of the priority a government assigns to a level of education relative to other levels. Enrolment and the relative costs per student between different levels of education should be also taken into account.\n\n### Limitations and exceptions:\nData disaggregated by level of education are estimates in some instances. It is often difficult to separate lower from upper secondary education expenditure, or pre-primary from primary.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://uis.unesco.org/bdds, publisher: UNESCO Institute for Statistics (UIS). Indicator SE.XPD.TERT.ZS (https://data.worldbank.org/indicator/SE.XPD.TERT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.TERT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of government expenditure on education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of government expenditure on education" } }, "se_xpd_totl_gb_zs": { "title": "Government expenditure on education, total (% of government expenditure)", "description_from_producer": "General government expenditure on education (current, capital, and transfers) is expressed as a percentage of total general government expenditure on all sectors (including health, education, social services, etc.). It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Expenditure on education, total (% of government expenditure) is calculated by dividing total government expenditure on education by the total government expenditure on all sectors and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nInformation regarding educational expenditures is obtained from national governments through their responses to the annual survey on formal education conducted by the UNESCO Institute for Statistics (UIS). The data provided for this questionnaire often originates from the annual financial reports of the Ministry of Finance or the Ministry of Education, or from the national accounts compiled by the National Statistical Office. Additionally, comprehensive data on general government expenditure across all sectors are sourced from the International Monetary Fund's (IMF) World Economic Outlook database, which undergoes an annual update.\nStatistical concept(s): A greater allocation of government funds towards education reflects a significant emphasis on educational priorities in comparison to other public sector investments. It is important to consider, however, that the capacity of governments to spend varies, resulting in differing budget sizes. Additionally, demographic factors such as the age distribution within a country can influence the proportion of spending on education versus other areas like health or social security.\n\n### Development relevance:\nInvestment in education serves as a driving force for economic growth, enhancement of productivity, and the promotion of individual and collective prosperity. This indicator is instrumental in evaluating the extent to which a government prioritizes education, whether over time or in relation to other nations. Furthermore, it reflects the government's dedication to the investment in human capital development.\n\n### Limitations and exceptions:\nData on government expenditure on education may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries). In addition, definitions and methods of data on total general government expenditure may differ across countries.", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.XPD.TOTL.GB.ZS (https://data.worldbank.org/indicator/SE.XPD.TOTL.GB.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.TOTL.GB.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of government expenditure", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of government expenditure" } }, "se_xpd_totl_gd_zs": { "title": "Government expenditure on education, total (% of GDP)", "description_from_producer": "General government expenditure on education (current, capital, and transfers) is expressed as a percentage of GDP. It includes expenditure funded by transfers from international sources to government. General government usually refers to local, regional and central governments.\n\n### Aggregation method:\nMedian\n\n### Statistical concept and methodology:\nMethodology: Government expenditure on education, total (% of GDP) is calculated by dividing total government expenditure for all levels of education by the GDP, and multiplying by 100. Aggregate data are based on World Bank estimates.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nInformation pertaining to educational expenditures is sourced from national governments, which provide the data in response to the annual survey conducted by the UNESCO Institute for Statistics (UIS) or through the joint UNESCO-OECD-Eurostat (UOE) data collection initiative. The responses to the questionnaire regarding educational spending are typically derived from the annual financial statements issued by either the Ministry of Finance or the Ministry of Education, or from the national accounts maintained by the National Statistical Office. Additionally, data concerning GDP and overall government expenditure are accessible via the IMF\u2019s World Economic Outlook database, which is updated annually.\nStatistical concept(s): Generally, elevated levels of the indicator suggest that a government places a high priority on educational policy. Values ranging from 4% to 6% are indicative of a country achieving the benchmark set forth by the Education 2030 Framework for Action (https://unesdoc.unesco.org/ark:/48223/pf0000245656).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEducational expenditure encompasses spending on fundamental educational goods and services, including teaching personnel, school infrastructure, textbooks, and instructional materials, as well as on ancillary educational goods and services such as support services, general administration, and other related activities.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFunding for education may originate from public sources, encompassing all government ministries and agencies that finance or support educational programs within the country, as well as from international and private sources, such as household contributions.\n\n### Development relevance:\nInvestment in education acts as a driving force for economic growth, enhances productivity, and promotes the betterment of individual and collective welfare. This indicator evaluates the extent to which a government prioritizes education in relation to its overall economic prosperity.\n\n### Limitations and exceptions:\nData may refer to spending by the ministry of education only (excluding spending on educational activities by other ministries).", "origins": [ { "producer": "UNESCO UIS Data API, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Data API, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources, note: The data are obtained through the UIS API. Detailed documentation is available at: https://api.uis.unesco.org/api/public/documentation/, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2026-03-19, date published: 2026-02. Indicator SE.XPD.TOTL.GD.ZS (https://data.worldbank.org/indicator/SE.XPD.TOTL.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SE.XPD.TOTL.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "sg_dmk_alld_fn_zs": { "title": "Women participating in the three decisions (own health care, major household purchases, and visiting family) (% of women age 15-49)", "description_from_producer": "Women participating in the three decisions (own health care, major household purchases, and visiting family) is the percentage of currently married women aged 15-49 who say that they alone or jointly have the final say in all of the three decisions (own health care, large purchases and visits to family, relatives, and friends).\n\n### Statistical concept and methodology:\nMethodology: Women participating in the three decisions (own health care, major household purchases, and visiting family) is the number of currently married women aged 15-49 who say they alone or jointly have the final say in the three decisions, expressed as percentage of currently married women age 15-49 who have been interviewed and It\u2019s derived by dividing the number of currently married women aged 15-49 who responded they alone or jointly have the final say in the three decisions by total number of currently married women age 15-49 who have been interviewed.\nStatistical concept(s): This indicator assesses the level of women's participation in household decision-making. It emphasizes the importance of decisions regarding their own health care, which are deemed essential to women's self-interest. The indicator also evaluates women's involvement in making substantial economic decisions, such as those related to significant purchases, to gauge their economic decision-making power within the household. Additionally, it measures women's autonomy in deciding on visits to family or friends, which can indicate their freedom of movement and the ability to engage with their birth family.\n\n### Development relevance:\nWomen\u2018s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women\u2018s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development.", "origins": [ { "producer": "Demographic and Health Surveys (DHS), The DHS Program (ICF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys (DHS), Demographic and Health Surveys (DHS) Program, uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_DMAK_W_3DC; \tIndicator name from the original source: Final say in all of the decisions [Women], publisher: The DHS program (ICF), type: API, date accessed: 2023-08-18. Indicator SG.DMK.ALLD.FN.ZS (https://data.worldbank.org/indicator/SG.DMK.ALLD.FN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.DMK.ALLD.FN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of women age 15-49", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of women age 15-49" } }, "sg_dmk_srcr_fn_zs": { "title": "Women making their own informed decisions regarding sexual relations, contraceptive use and reproductive health care (% of women age 15-49)", "description_from_producer": "Proportion of women ages 15-49 years (married or in union) who make their own decision on all three selected areas i.e. can say no to sexual intercourse with their husband or partner if they do not want; decide on use of contraception; and decide on their own health care. Only women who provide a \u201cyes\u201d answer to all three components are considered as women who \u201cmake her own decisions regarding sexual and reproductive\u201d.\n\n### Statistical concept and methodology:\nMethodology: Numerator of the indicator is number of married or in union women ages 15-49 who have been interviewed and satisfy all three empowerment criteria: 1)who can say 'no' to sex; and 2)for whom the decision on contraception is not mainly made by the husband/partner; and 3) for whom decision on health care for themselves is not usually made by the husband/partner or someone else. Denominator of the indicator is the total number of women ages 15-49 who are married or in union and who have been interviewed.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe data for this indicator are primarily sourced from nationally representative Demographic and Health Surveys (DHS).\nStatistical concept(s): A woman is deemed to possess autonomy in reproductive health decision-making and to be empowered to assert her reproductive rights when she has the ability to: (1) make decisions regarding her own health care, independently or in conjunction with her husband or partner, (2) determine the use or non-use of contraception, on her own or together with her husband or partner, and (3) refuse sexual relations with her husband or partner if she chooses.\n\n### Development relevance:\nWomen\u2018s participation in decisions being made in their own households, that is households in which they usually live with their spouse and/or children with or without others, is widely accepted as a universal indicator of women\u2018s empowerment. The ability of women to make decisions that affect their personal circumstances is an essential element of their empowerment and serves as an important contributor to their overall development.\n\n### Limitations and exceptions:\nCurrent estimates of the indicator are based on currently married or in union women of reproductive age (15-49 years old) who are using any type of contraception. In the current Demographic and Health Surveys (DHS), the question on decision-making on use of contraception is only asked to women who are currently using contraception. Because the questions on decision- making on sexual relations and health care are restricted to women (15-49) currently married or in union, the denominator for Indicator 5.6.1 is women 15-49, who are currently married or in union and currently using contraception. However, agreement has been reached with Macro/ICF for upcoming DHS surveys to ask the question on decision on use of contraception to all married/ in union women aged 15-49 years, whether they are currently using any contraception or not. The DHS model questionnaire for Phase 7 already includes the question on decision-making for women who are not currently using any contraception.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 5.6.1[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Demographic and Health Surveys (DHS) and UN Statistics Division, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys compiled by United Nations Population Fund, United Nations (UN), uri: https://unstats.un.org/sdgs/UNSDGAPIV5/swagger/index.html, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of women aged 15\u201349 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care, publisher: UN Statistics Division, type: API, date accessed: 2026-06-17, date published: 2026-03-27. Indicator SG.DMK.SRCR.FN.ZS (https://data.worldbank.org/indicator/SG.DMK.SRCR.FN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.DMK.SRCR.FN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of women age 15-49", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of women age 15-49" } }, "sg_gen_parl_zs": { "title": "Proportion of seats held by women in national parliaments (%)", "description_from_producer": "Women in parliaments are the percentage of parliamentary seats in a single or lower chamber held by women.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of seats held by women in national parliaments is the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats; it is derived by dividing the total number of seats occupied by women by the total number of seats in parliament.\nStatistical concept(s): This indicator assesses the extent to which women are provided with equal opportunities to participate in parliamentary decision-making processes. It applies to the sole chamber of unicameral national parliaments and the lower chamber in the case of bicameral systems. The upper chamber in bicameral parliaments is not included in this measure. Parliamentary seats are typically occupied by individuals who are victorious in general elections, though they can also be acquired through nomination, appointment, indirect election, member rotation, or by-elections. The term 'seats' refers to the total count of parliamentary mandates or the total number of parliament members.\n\n### Development relevance:\nDespite much progress in recent decades, gender inequalities remain pervasive in many dimensions of life - worldwide. But while disparities exist throughout the world, they are most prevalent in developing countries. Gender inequalities in the allocation of such resources as education, health care, nutrition, and political voice matter because of the strong association with well-being, productivity, and economic growth. These patterns of inequality begin at an early age, with boys routinely receiving a larger share of education and health spending than do girls, for example.\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen are vastly underrepresented in decision-making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\n\n\n\n\n\n\n\n\n\n\n\n\nA strong and vibrant democracy is possible only when parliament is fully inclusive of the population it represents. Parliaments cannot consider themselves inclusive, however, until they can boast the full participation of women. This is not just about women's right to equality and their contribution to the conduct of public affairs, but also about using women's resources and potential to determine political and development priorities that benefit societies and the global community.\n\n### Limitations and exceptions:\nThe number of countries covered varies with suspensions or dissolutions of parliaments. There can be difficulties in obtaining information on by-election results and replacements due to death or resignation. These changes are ad hoc events which are more difficult to keep track of. By-elections, for instance, are often not announced internationally as general elections are. Parliaments vary considerably in their internal workings and procedures, however, generally legislate, oversee government and represent the electorate. In terms of measuring women's contribution to political decision making, this indicator may not be sufficient because some women may face obstacles in fully and efficiently carrying out their parliamentary mandate.\n\n\n\n\n\n\n\n\n\n\n\nThe data is compiled by the Inter-Parliamentary Union on the basis of information provided by National Parliaments. The percentages do not take into account the case of parliaments for which no data was available at that date. Information is available in all countries where a national legislature exists and therefore does not include parliaments that have been dissolved or suspended for an indefinite period.\n\n### Other notes:\nRelevance to gender indicator: Women are vastly underrepresented in decision making positions in government, although there is some evidence of recent improvement. Gender parity in parliamentary representation is still far from being realized. Without representation at this level, it is difficult for women to influence policy.\n\nThis is the Sustainable Development Goal indicator 5.5.1 (a). [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Inter-Parliamentary Union (IPU) monthly ranking of women in national parliaments, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Monthly ranking of women in national parliaments, Inter-Parliamentary Union (IPU), uri: https://data.ipu.org/women-ranking/, note: For the year of 1998, the data is as of August 10, 1998., type: Excel, date accessed: 2026-03-29. Indicator SG.GEN.PARL.ZS (https://data.worldbank.org/indicator/SG.GEN.PARL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.GEN.PARL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sg_tim_uwrk_fe": { "title": "Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)", "description_from_producer": "The average time women spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others.\n\n### Statistical concept and methodology:\nMethodology: The proportion of time allocated to unpaid domestic and caregiving tasks is determined by dividing the daily average time spent on such activities by the total number of hours in a day (24 hours). The data for this indicator are presented as a daily time proportion. For instance, if women ages 15+ dedicate 10% of their day to unpaid domestic and caregiving work, while men of the same age bracket allocate 1%, this translates to women spending an average of 2.4 hours (or 2 hours and 24 minutes) and men spending 14.4 minutes per day on these tasks.\n\n\n\n\n\n\n\nTo ascertain the daily average, weekly data are averaged across all seven days.\nStatistical concept(s): This indicator measures the average amount of unpaid domestic and care work as a proportion in a day. The objective of this indicator is to quantify the time allocation of both women and men to unpaid tasks, thereby recognizing the value of all forms of work, irrespective of monetary compensation. Furthermore, it serves as a gauge for gender equality by revealing the disparity in time spent by women and men on unpaid activities, such as household chores, caregiving, and childcare.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe daily average is derived from a mean calculated over the data collection reference period, which does not imply that individuals allocate the specified amounts of time to these activities every day.\n\n### Development relevance:\nWomen often spend disproportionately more time on unpaid domestic and care work than men. This unequal division of responsibilities is correlated with gender differences in economic opportunities, including low female labor force participation, occupational sex segregation, and earnings differentials. The need for a gender balance in the distribution of unpaid domestic and care work has been increasingly recognized and the Sustainable Development Goals address the issue in the target 5.4.\n\n### Limitations and exceptions:\nData may not be strictly comparable across countries as the methods and sampling involved for data collection may differ.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "National statistical offices and UN Statistics Division, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "National statistical offices or national database and publications, United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of time spent on unpaid domestic chores and care work, by sex, age and location (%), publisher: UN Statistics Division, type: Excel, date accessed: 46190, date published: 2026-03-27. Indicator SG.TIM.UWRK.FE (https://data.worldbank.org/indicator/SG.TIM.UWRK.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.TIM.UWRK.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of 24 hour day", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of 24 hour day" } }, "sg_tim_uwrk_ma": { "title": "Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)", "description_from_producer": "The average time men spend on household provision of services for own consumption. Data are expressed as a proportion of time in a day. Domestic and care work includes food preparation, dishwashing, cleaning and upkeep of a dwelling, laundry, ironing, gardening, caring for pets, shopping, installation, servicing and repair of personal and household goods, childcare, and care of the sick, elderly or disabled household members, among others.\n\n### Statistical concept and methodology:\nMethodology: The proportion of time allocated to unpaid domestic and caregiving tasks is determined by dividing the daily average time spent on such activities by the total number of hours in a day (24 hours). The data for this indicator are presented as a daily time proportion. For instance, if women ages 15+ dedicate 10% of their day to unpaid domestic and caregiving work, while men of the same age bracket allocate 1%, this translates to women spending an average of 2.4 hours (or 2 hours and 24 minutes) and men spending 14.4 minutes per day on these tasks.\n\n\n\n\n\n\n\nTo ascertain the daily average, weekly data are averaged across all seven days.\nStatistical concept(s): This indicator measures the average amount of unpaid domestic and care work as a proportion in a day. The objective of this indicator is to quantify the time allocation of both women and men to unpaid tasks, thereby recognizing the value of all forms of work, irrespective of monetary compensation. Furthermore, it serves as a gauge for gender equality by revealing the disparity in time spent by women and men on unpaid activities, such as household chores, caregiving, and childcare.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe daily average is derived from a mean calculated over the data collection reference period, which does not imply that individuals allocate the specified amounts of time to these activities every day.\n\n### Development relevance:\nWomen often spend disproportionately more time on unpaid domestic and care work than men. This unequal division of responsibilities is correlated with gender differences in economic opportunities, including low female labor force participation, occupational sex segregation, and earnings differentials. The need for a gender balance in the distribution of unpaid domestic and care work has been increasingly recognized and the Sustainable Development Goals address the issue in the target 5.4.\n\n### Limitations and exceptions:\nData may not be strictly comparable across countries as the methods and sampling involved for data collection may differ.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 5.4.1[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "United Nations Statistics Division and national statistical offices, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "National statistical offices or national database and publications compiled by United Nations Statistics Division., United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: SH_FPL_INFM; \tIndicator name from the original source: Proportion of time spent on unpaid domestic chores and care work, by sex, age and location (%), publisher: UN Statistics Division, type: Excel, date accessed: 46190, date published: 2026-03-27. Indicator SG.TIM.UWRK.MA (https://data.worldbank.org/indicator/SG.TIM.UWRK.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.TIM.UWRK.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of 24 hour day", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of 24 hour day" } }, "sg_vaw_1549_zs": { "title": "Proportion of women subjected to physical and/or sexual violence in the last 12 months (% of ever-partnered women ages 15-49)", "description_from_producer": "Proportion of women subjected to physical and/or sexual violence in the last 12 months is the percentage of ever partnered women age 15-49 who are subjected to physical violence, sexual violence or both by a current or former intimate partner in the last 12 months.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated by the number of ever-partnered women (ages 15 and above) subjected to any act of physical violece, sexual violence or both divided by a current or former intimate partner in the previous 12 months divided by the number of ever-partnered women (aged 15 years and above) in the population multiplied by 100. The main source of data are from the Demographic and Health Surveys (DHS), specialized surveys on violence against women and crime victimasation surveys.\nStatistical concept(s): Physical violence is defined as acts that can physically hurt the victim, including, but not limited to: being slapped or having something thrown at you that could hurt you; being pushed or shoved; being hit with a fist or something else that could hurt; being kicked, dragged or beaten up; being choked or burnt on purpose; and/or being threatened with or actually having a gun, knife or other weapon used on you. Sexual violence is operationalized as: being physically forced to have sexual intercourse when you do not want to; having sexual intercourse out of fear for what your partner might do or through coercion; and/or being forced to do something sexual that you consider humiliating or degrading (Reference: WHO, Violence Against Women Prevalence Estimates, 2018. https://www.who.int/publications/i/item/9789240022256).\n\n\n\n\n\n\n\n\n\n\n\nCurrent intimate partner includes current or most recent husbands of ever-married women (and men they live with as if married) and the current intimate partner of never-married women. Former husband/intimate partner is a husband (or partner she is living with as if married) other than the current husband (or man she is living with as if married) for currently married women, any intimate partner for never-married women who do not currently have an intimate partner, and a husband/partner other than the most recent husband/partner for divorced, separated, or widowed women.\n\n### Development relevance:\nViolence against women is an obstacle to the achievement of the objectives of equality, development and peace. It both violates and impairs or nullifies the enjoyment by women of their human rights and fundamental freedoms. Tolerance and experience of domestic violence are significant barriers to the empowerment of women and it has a negative health consequences for victims, especially with respect to the reproductive health of women and the physical, emotional, and mental health of their children.\n\n### Limitations and exceptions:\nCollecting valid, reliable, and ethical data on domestic violence poses particular challenges because what constitutes violence or abuse varies across cultures and among individuals. In addition, a culture of silence usually surrounds domestic violence and can affect reporting.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 5.2.1[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "United Nations Statistics Division and World Health Organization, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "United Nations (UN), uri: https://unstats.un.org/sdgs/dataportal/database, note: Indicator code from the original source: VC_VAW_MARR; \tIndicator name from the original source: Proportion of ever-partnered women and girls subjected to physical and/or sexual violence by a current or former intimate partner in the previous 12 months, by age (%)\n\n\n\n\n\n\n\n\n, publisher: UN Statistics Division, type: API;\nGlobal Health Observatory, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/proportion-of-ever-partnered-women-and-girls-aged-15-49-years-subjected-to-physical-and-or-sexual-violence-by-a-current-or-former-intimate-partner-in-the-previous-12-months, publisher: WHO. Indicator SG.VAW.1549.ZS (https://data.worldbank.org/indicator/SG.VAW.1549.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.VAW.1549.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of women age 15-49", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of women age 15-49" } }, "sg_vaw_argu_zs": { "title": "Women who believe a husband is justified in beating his wife when she argues with him (%)", "description_from_producer": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she argues with him.\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women.\n\n### Development relevance:\nThe collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children.\n\n### Other notes:\nSupportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles.", "origins": [ { "producer": "Demographic and Health Surveys (DHS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys (DHS), Demographic and Health Surveys (DHS) Program, uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_ARG; \tIndicator name from the original source: Wife beating justified if she argues with him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 46155. Indicator SG.VAW.ARGU.ZS (https://data.worldbank.org/indicator/SG.VAW.ARGU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.VAW.ARGU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sg_vaw_burn_zs": { "title": "Women who believe a husband is justified in beating his wife when she burns the food (%)", "description_from_producer": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she burns the food.\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women.\n\n### Development relevance:\nThe collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children.\n\n### Other notes:\nSupportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles.", "origins": [ { "producer": "Demographic and Health Surveys (DHS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys (DHS), Demographic and Health Surveys (DHS) Program, uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_BFD; \tIndicator name from the original source: Wife beating justified if she burns the food [Women], publisher: The DHS Program (ICF), type: API, date accessed: 46155. Indicator SG.VAW.BURN.ZS (https://data.worldbank.org/indicator/SG.VAW.BURN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.VAW.BURN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sg_vaw_goes_zs": { "title": "Women who believe a husband is justified in beating his wife when she goes out without telling him (%)", "description_from_producer": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she goes out without telling him.\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women.\n\n### Development relevance:\nThe collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children.\n\n### Other notes:\nSupportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles.", "origins": [ { "producer": "Demographic and Health Surveys (DHS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys (DHS), Demographic and Health Surveys (DHS) Program, uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_OUT; \tIndicator name from the original source: Wife beating justified if she goes out without telling him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 46155. Indicator SG.VAW.GOES.ZS (https://data.worldbank.org/indicator/SG.VAW.GOES.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.VAW.GOES.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sg_vaw_negl_zs": { "title": "Women who believe a husband is justified in beating his wife when she neglects the children (%)", "description_from_producer": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she neglects the children.\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women.\n\n### Development relevance:\nThe collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children.\n\n### Other notes:\nSupportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles.", "origins": [ { "producer": "Demographic and Health Surveys (DHS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys (DHS), Demographic and Health Surveys (DHS) Program, uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_NEG; \tIndicator name from the original source: Wife beating justified if she neglects the children [Women], publisher: The DHS Program (ICF), type: API, date accessed: 46155. Indicator SG.VAW.NEGL.ZS (https://data.worldbank.org/indicator/SG.VAW.NEGL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.VAW.NEGL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sg_vaw_reas_zs": { "title": "Women who believe a husband is justified in beating his wife (any of five reasons) (%)", "description_from_producer": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner for any of the following five reasons: argues with him; refuses to have sex; burns the food; goes out without telling him; or when she neglects the children.\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife for any five reasons by total number of women ages 15-49 who have been interviewed. The five reasons include argues with him; refuses to have sex; burns the food; goes out without telling him; or when she neglects the children. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women.\n\n### Development relevance:\nThe collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children.\n\n### Other notes:\nSupportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles.", "origins": [ { "producer": "Demographic and Health Surveys (DHS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys (DHS), Demographic and Health Surveys (DHS) Program, uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_AGR; \tIndicator name from the original source: Wife beating justified for at least one specific reason [Women], publisher: The DHS Program (ICF), type: API, date accessed: 46155. Indicator SG.VAW.REAS.ZS (https://data.worldbank.org/indicator/SG.VAW.REAS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.VAW.REAS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sg_vaw_refu_zs": { "title": "Women who believe a husband is justified in beating his wife when she refuses sex with him (%)", "description_from_producer": "Percentage of women ages 15-49 who believe a husband/partner is justified in hitting or beating his wife/partner when she refuses sex with him.\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing the number of women ages 15-49 who agree that a husband is justified in hitting or beating his wife when she burns the food by total number of women ages 15-49 who have been interviewed. The data for this indicator are sourced from Demographic and Health Surveys (DHS).\nStatistical concept(s): This indicator is one of the sets of attitude questions concerning justifications of a husband beating his wife in Demographic and Health Surveys. These questions aim to understand women's perspectives on gender equality. Acceptance of wife beating indicates an underlying acceptance of a lower status for women.\n\n### Development relevance:\nThe collection of this data is crucial for assessing the degree to which women possess the empowerment necessary to exert control over their own actions, bodies, and sexual autonomy. Societal attitudes that condone the physical abuse of wives by their husbands reflect a diminished status of women and contribute to their disempowerment within domestic and intimate relationships. The empowerment and autonomy of women are vital to achieving sustainable development goals, with the elimination of violence against women being a specific target outlined in SDG 5.2. Furthermore, a woman's autonomy can affect the health of household members and the educational attainment of children.\n\n### Other notes:\nSupportive attitudes should not automatically be seen as approval of wife-beating, nor do they mean that a woman or girl will inevitably become a victim of domestic violence. Instead, these attitudes should be viewed as reflecting the level of social acceptance of such practices. This acceptance can be shaped by the belief that women and girls hold a lower status in society compared to men and boys, or by the expectation that they should adhere to specific gender roles.", "origins": [ { "producer": "Demographic and Health Surveys (DHS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys (DHS), Demographic and Health Surveys (DHS) Program, uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: WE_AWBT_W_REF; \tIndicator name from the original source: Wife beating justified if she refuses to have sex with him [Women], publisher: The DHS Program (ICF), type: API, date accessed: 46155. Indicator SG.VAW.REFU.ZS (https://data.worldbank.org/indicator/SG.VAW.REFU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SG.VAW.REFU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sh_alc_pcap_fe_li": { "title": "Total alcohol consumption per capita, female (liters of pure alcohol, projected estimates, female 15+ years of age)", "description_from_producer": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries.\n\n### Development relevance:\nAccording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions.\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Global Health Observatory Data Repository - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.ALC.PCAP.FE.LI (https://data.worldbank.org/indicator/SH.ALC.PCAP.FE.LI). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.ALC.PCAP.FE.LI", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_alc_pcap_li": { "title": "Total alcohol consumption per capita (liters of pure alcohol, projected estimates, 15+ years of age)", "description_from_producer": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries.\n\n### Development relevance:\nAccording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.5.2[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Global Health Observatory Data Repository - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.ALC.PCAP.LI (https://data.worldbank.org/indicator/SH.ALC.PCAP.LI). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.ALC.PCAP.LI", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "liters of pure alcohol, projected estimates, 15+ years of age", "display": { "name": "Alcohol consumption", "unit": "liters of pure alcohol, projected estimates, 15+ years of age" }, "presentation": { "title_public": "Total alcohol consumption per capita", "title_variant": "In liters of pure alcohol, projected estimates, 15+ years of age" } }, "sh_alc_pcap_ma_li": { "title": "Total alcohol consumption per capita, male (liters of pure alcohol, projected estimates, male 15+ years of age)", "description_from_producer": "Total alcohol per capita consumption is defined as the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates for the total alcohol consumption are produced by summing up the 3-year average per capita (15+) recorded alcohol consumption and an estimate of per capita (15+) unrecorded alcohol consumption for a calendar year. Tourist consumption takes into account tourists visiting the country and inhabitants visiting other countries.\n\n### Development relevance:\nAcoording to the World Health Organization, alcohol consumption is a causal factor in more than 200 disease and injury conditions. In the world, an estimated 3 million deaths are from harmful use of alcohols every year. Drinking alcohol is associated with a risk of developing health problems such as mental and behavioural disorders, including alcohol dependence, major noncommunicable diseases such as liver cirrhosis, some cancers and cardiovascular diseases, as well as injuries resulting from violence and road clashes and collisions.\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.5.2 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Global Health Observatory Data Repository - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.ALC.PCAP.MA.LI (https://data.worldbank.org/indicator/SH.ALC.PCAP.MA.LI). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.ALC.PCAP.MA.LI", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_anm_allw_zs": { "title": "Prevalence of anemia among women of reproductive age (% of women ages 15-49)", "description_from_producer": "Prevalence of anemia among women of reproductive age refers to the combined prevalence of both non-pregnant with haemoglobin levels below 12 g/dL and pregnant women with haemoglobin levels below 11 g/dL.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on the prevalence of anaemia and/or mean haemoglobin levels in women of reproductive age, collected between 1995 and 2019, were obtained from 408 population-representative data sources across 124 countries worldwide. A Bayesian hierarchical mixture model was employed to estimate haemoglobin distributions, systematically addressing missing data, non-linear time trends, and the representativeness of data sources. Full details on data sources are available on the GHO Anaemia page. Detailed information on the statistical methods can be found in the following reference: Finucane MM, Paciorek CJ, Stevens GA EM. Semiparametric Bayesian density estimation with disparate data sources: a meta-analysis of global childhood undernutrition. J Am Stat Assoc. 2015;110(511):889\u2013901.", "origins": [ { "producer": "Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO). Indicator SH.ANM.ALLW.ZS (https://data.worldbank.org/indicator/SH.ANM.ALLW.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.ANM.ALLW.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of women ages 15-49", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of women ages 15-49" } }, "sh_anm_chld_zs": { "title": "Prevalence of anemia among children (% of children ages 6-59 months)", "description_from_producer": "Prevalence of anemia, children ages 6-59 months, is the percentage of children ages 6-59 months whose hemoglobin level is less than 110 grams per liter, adjusted for altitude.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on anemia are compiled by the WHO, and a statistical model was used to estimate trends. WHO\u2019s hemoglobin threshold concentration in blood was used.\n\n### Limitations and exceptions:\nData for blood haemoglobin concentrations are still limited, compared to other nutritional indicators such as child anthropometry. As a result, the estimates may not capture the full variation across countries and regions.\n\n### Other notes:\nAnemia is defined as a low blood haemoglobin concentration. Anaemia may result from a number of causes, with the most significant contributor being iron deficiency. Anaemia resulting from iron deficiency adversely affects cognitive and motor development and causes fatigue and low productivity. Children under age 5 and pregnant women have the highest risk for anemia.", "origins": [ { "producer": "Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO). Indicator SH.ANM.CHLD.ZS (https://data.worldbank.org/indicator/SH.ANM.CHLD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.ANM.CHLD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_anm_nprg_zs": { "title": "Prevalence of anemia among non-pregnant women (% of women ages 15-49)", "description_from_producer": "Prevalence of anemia, non-pregnant women, is the percentage of non-pregnant women whose hemoglobin level is less than 120 grams per liter at sea level.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on the prevalence of anaemia and/or mean haemoglobin levels in women of reproductive age, collected between 1995 and 2019, were obtained from 408 population-representative data sources across 124 countries worldwide. A Bayesian hierarchical mixture model was employed to estimate haemoglobin distributions, systematically addressing missing data, non-linear time trends, and the representativeness of data sources. Full details on data sources are available on the GHO Anaemia page. Detailed information on the statistical methods can be found in the following reference: Finucane MM, Paciorek CJ, Stevens GA EM. Semiparametric Bayesian density estimation with disparate data sources: a meta-analysis of global childhood undernutrition. J Am Stat Assoc. 2015;110(511):889\u2013901.", "origins": [ { "producer": "Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO). Indicator SH.ANM.NPRG.ZS (https://data.worldbank.org/indicator/SH.ANM.NPRG.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.ANM.NPRG.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of women ages 15-49", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of women ages 15-49" } }, "sh_con_1524_fe_zs": { "title": "Condom use, population ages 15-24, female (% of females ages 15-24)", "description_from_producer": "Condom use, female is the percentage of the female population ages 15-24 who used a condom at last intercourse in the last 12 months.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Household Surveys\nStatistical concept(s): When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs).\n\n### Development relevance:\nAccording to UNAIDS estimates, HIV incidence has fallen in many of the most severely affected countries because adolescents and young people are adopting safer sexual practices and more young people living with HIV are accessing treatment to lower their viral load. When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs).", "origins": [ { "producer": "Demographic and Health Surveys (DHS) and UNAIDS, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys;\nUNAIDS, Joint United Nations Programme on HIV/AIDS (UNAIDS). Indicator SH.CON.1524.FE.ZS (https://data.worldbank.org/indicator/SH.CON.1524.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.CON.1524.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of females ages 15-24", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of females ages 15-24" } }, "sh_con_1524_ma_zs": { "title": "Condom use, population ages 15-24, male (% of males ages 15-24)", "description_from_producer": "Condom use, male is the percentage of the male population ages 15-24 who used a condom at last intercourse in the last 12 months.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Household Surveys\nStatistical concept(s): When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs).\n\n### Development relevance:\nAccording to UNAIDS estimates, HIV incidence has fallen in many of the most severely affected countries because adolescents and young people are adopting safer sexual practices and more young people living with HIV are accessing treatment to lower their viral load. When used the right way every time, condoms are highly effective in preventing HIV and other sexually transmitted diseases (STDs).", "origins": [ { "producer": "Demographic and Health Surveys (DHS) and UNAIDS, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys;\nUNAIDS, Joint United Nations Programme on HIV/AIDS (UNAIDS). Indicator SH.CON.1524.MA.ZS (https://data.worldbank.org/indicator/SH.CON.1524.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.CON.1524.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of males ages 15-24", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of males ages 15-24" } }, "sh_dth_0509": { "title": "Number of deaths ages 5-9 years", "description_from_producer": "Number of deaths of children ages 5-9 years\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DTH.0509 (https://data.worldbank.org/indicator/SH.DTH.0509). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DTH.0509", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dth_1014": { "title": "Number of deaths ages 10-14 years", "description_from_producer": "Number of deaths of adolescents ages 10-14 years\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DTH.1014 (https://data.worldbank.org/indicator/SH.DTH.1014). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DTH.1014", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dth_1519": { "title": "Number of deaths ages 15-19 years", "description_from_producer": "Number of deaths of adolescents ages 15-19 years\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DTH.1519 (https://data.worldbank.org/indicator/SH.DTH.1519). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DTH.1519", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dth_2024": { "title": "Number of deaths ages 20-24 years", "description_from_producer": "Number of deaths of youths ages 20-24 years\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DTH.2024 (https://data.worldbank.org/indicator/SH.DTH.2024). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DTH.2024", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dth_comm_zs": { "title": "Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)", "description_from_producer": "Cause of death refers to the share of all deaths for all ages by underlying causes. Communicable diseases and maternal, prenatal and nutrition conditions include infectious and parasitic diseases, respiratory infections, and nutritional deficiencies such as underweight and stunting.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on cause of death are compiled by the World Health Organization (WHO), using information from civil registration and vital statistics systems (CRVS), local health and demographic studies and other sources, supplemented by vital registration and verbal autopsy in communities as well as regular household health surveys. To address incomplete or inconsistent reporting\u2014particularly in countries with limited vital registration coverage\u2014WHO applies statistical models that account for underreporting, demographic differentials, and expected cause-of-death distribution. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, refer to the WHO Global Health Estimates methodological documentation.\nStatistical concept(s): Causes of death in the Global Health Estimates (GHE) are classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) or ICD-9. Reported data from countries are mapped to a standardized cause list structured by WHO in a three-level hierarchy. Each death is assigned to a single underlying cause based on ICD rules. To ensure internal consistency, all cause-specific mortality estimates are constrained to fit within an all-cause mortality envelope derived from demographic estimates prepared by the UN Population Division and WHO. The cause list is designed to be mutually exclusive and collectively exhaustive, allowing full decomposition of total mortality by age, sex, country, and year.\n\nFor countries with incomplete, poor-quality, or no usable cause-of-death data, WHO uses a compositional cause modeling strategy. This approach estimates the distribution of causes within broad cause groups based on epidemiological and demographic covariates. Redistribution algorithms are applied to correct for misclassification or ill-defined causes. All modeling approaches are aligned with WHO\u2019s goal of maximizing comparability, transparency, and usability of mortality data across settings with diverse data availability and health system capacities.\n\n### Development relevance:\nUnderstanding the distribution of causes of death is critical for health planning, policy formulation, and the allocation of resources. Information on the cause of death\u2014 communicable diseases and maternal, prenatal and nutrition conditions, non-communicable diseases (NCDs), injury, and, in conjunction with other COVID-19 pandemic-related outcomes\u2014provides a foundation for assessing epidemiological transitions, identifying emerging health threats, and monitoring progress toward national and global health goals, including the Sustainable Development Goals (SDGs); in particular, SDG Target 3.4 aims to reduce premature mortality from NCDs, while communicable diseases remain a key focus under Targets 3.3 and 3.8, which address disease-specific burdens and universal health coverage.\n\nInformation on the cause of death is especially important for low- and middle-income countries, where health systems must simultaneously address both infectious disease burdens and the rising prevalence of NCDs and injury-related mortality. Disaggregated cause-of-death data enable governments and development partners to prioritize interventions, assess health system performance, and guide investments in prevention and care. Moreover, monitoring mortality by cause helps identify disparities across population groups and geographies, ensuring that public health responses are equitable and data-driven.\n\n### Limitations and exceptions:\nLimited data availability on health status remains a major constraint in assessing health conditions in low-and middle-income countries. Surveillance systems are often weak or absent for major public health issues, and available estimates of disease prevalence and incidence may be incomplete or unreliable. National capacities and commitments to data collection and reporting vary significantly. To address these gaps and enhance reliability and comparability, the World Health Organization (WHO) produces estimates using epidemiological models.\n\nThe COVID-19 pandemic introduced exceptional challenges. Even in countries with relatively complete vital registration systems, excess mortality may have been misclassified between COVID-19 and other causes of death. In countries lacking comprehensive registration systems, greater reliance on modelled estimates\u2014including categories such as \u201cother pandemic-related mortality\u201d\u2014may have resulted in underestimation or redistribution of deaths from specific causes, particularly cardiovascular diseases and other non-communicable diseases.", "origins": [ { "producer": "World Health Organization Global Health Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021. Indicator SH.DTH.COMM.ZS (https://data.worldbank.org/indicator/SH.DTH.COMM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DTH.COMM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sh_dth_imrt": { "title": "Number of infant deaths", "description_from_producer": "Number of infants dying before reaching one year of age.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DTH.IMRT (https://data.worldbank.org/indicator/SH.DTH.IMRT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DTH.IMRT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dth_injr_zs": { "title": "Cause of death, by injury (% of total)", "description_from_producer": "Cause of death refers to the share of all deaths for all ages by underlying causes. Injuries include unintentional and intentional injuries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on cause of death are compiled by the World Health Organization (WHO), using information from civil registration and vital statistics systems (CRVS), local health and demographic studies and other sources, supplemented by vital registration and verbal autopsy in communities as well as regular household health surveys. To address incomplete or inconsistent reporting\u2014particularly in countries with limited vital registration coverage\u2014WHO applies statistical models that account for underreporting, demographic differentials, and expected cause-of-death distribution. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, refer to the WHO Global Health Estimates methodological documentation.\nStatistical concept(s): Causes of death in the Global Health Estimates (GHE) are classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) or ICD-9. Reported data from countries are mapped to a standardized cause list structured by WHO in a three-level hierarchy. Each death is assigned to a single underlying cause based on ICD rules. To ensure internal consistency, all cause-specific mortality estimates are constrained to fit within an all-cause mortality envelope derived from demographic estimates prepared by the UN Population Division and WHO. The cause list is designed to be mutually exclusive and collectively exhaustive, allowing full decomposition of total mortality by age, sex, country, and year.\n\nFor countries with incomplete, poor-quality, or no usable cause-of-death data, WHO uses a compositional cause modeling strategy. This approach estimates the distribution of causes within broad cause groups based on epidemiological and demographic covariates. Redistribution algorithms are applied to correct for misclassification or ill-defined causes. All modeling approaches are aligned with WHO\u2019s goal of maximizing comparability, transparency, and usability of mortality data across settings with diverse data availability and health system capacities.\n\n### Development relevance:\nUnderstanding the distribution of causes of death is critical for health planning, policy formulation, and the allocation of resources. Information on the cause of death\u2014 communicable diseases and maternal, prenatal and nutrition conditions, non-communicable diseases (NCDs), injury, and, in conjunction other COVID-19 pandemic-related outcomes\u2014provides a foundation for assessing epidemiological transitions, identifying emerging health threats, and monitoring progress toward national and global health goals, including the Sustainable Development Goals (SDGs); in particular, SDG Target 3.4 aims to reduce premature mortality from NCDs, while communicable diseases remain a key focus under Targets 3.3 and 3.8, which address disease-specific burdens and universal health coverage.\n\nInformation on the cause of death is especially important for low- and middle-income countries, where health systems must simultaneously address both infectious disease burdens and the rising prevalence of NCDs and injury-related mortality. Disaggregated cause-of-death data enable governments and development partners to prioritize interventions, assess health system performance, and guide investments in prevention and care. Moreover, monitoring mortality by cause helps identify disparities across population groups and geographies, ensuring that public health responses are equitable and data-driven.\n\n### Limitations and exceptions:\nLimited data availability on health status remains a major constraint in assessing health conditions in low-and middle-income countries. Surveillance systems are often weak or absent for major public health issues, and available estimates of disease prevalence and incidence may be incomplete or unreliable. National capacities and commitments to data collection and reporting vary significantly. To address these gaps and enhance reliability and comparability, the World Health Organization (WHO) produces estimates using epidemiological models.\n\nThe COVID-19 pandemic introduced exceptional challenges. Even in countries with relatively complete vital registration systems, excess mortality may have been misclassified between COVID-19 and other causes of death. In countries lacking comprehensive registration systems, greater reliance on modelled estimates\u2014including categories such as \u201cother pandemic-related mortality\u201d\u2014may have resulted in underestimation or redistribution of deaths from specific causes, particularly cardiovascular diseases and other non-communicable diseases.", "origins": [ { "producer": "World Health Organization Global Health Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021. Indicator SH.DTH.INJR.ZS (https://data.worldbank.org/indicator/SH.DTH.INJR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DTH.INJR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sh_dth_mort": { "title": "Number of under-five deaths", "description_from_producer": "Number of children dying before reaching age five.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DTH.MORT (https://data.worldbank.org/indicator/SH.DTH.MORT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DTH.MORT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dth_ncom_zs": { "title": "Cause of death, by non-communicable diseases (% of total)", "description_from_producer": "Cause of death refers to the share of all deaths for all ages by underlying causes. Non-communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on cause of death are compiled by the World Health Organization (WHO), using information from civil registration and vital statistics systems (CRVS), local health and demographic studies and other sources, supplemented by vital registration and verbal autopsy in communities as well as regular household health surveys. To address incomplete or inconsistent reporting\u2014particularly in countries with limited vital registration coverage\u2014WHO applies statistical models that account for underreporting, demographic differentials, and expected cause-of-death distribution. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, refer to the WHO Global Health Estimates methodological documentation.\nStatistical concept(s): Causes of death in the Global Health Estimates (GHE) are classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) or ICD-9. Reported data from countries are mapped to a standardized cause list structured by WHO in a three-level hierarchy. Each death is assigned to a single underlying cause based on ICD rules. To ensure internal consistency, all cause-specific mortality estimates are constrained to fit within an all-cause mortality envelope derived from demographic estimates prepared by the UN Population Division and WHO. The cause list is designed to be mutually exclusive and collectively exhaustive, allowing full decomposition of total mortality by age, sex, country, and year.\n\nFor countries with incomplete, poor-quality, or no usable cause-of-death data, WHO uses a compositional cause modeling strategy. This approach estimates the distribution of causes within broad cause groups based on epidemiological and demographic covariates. Redistribution algorithms are applied to correct for misclassification or ill-defined causes. All modeling approaches are aligned with WHO\u2019s goal of maximizing comparability, transparency, and usability of mortality data across settings with diverse data availability and health system capacities.\n\n### Development relevance:\nUnderstanding the distribution of causes of death is critical for health planning, policy formulation, and the allocation of resources. Information on the cause of death\u2014 communicable diseases and maternal, prenatal and nutrition conditions, non-communicable diseases (NCDs), injury, and, in conjunction other COVID-19 pandemic-related outcomes\u2014provide a foundation for assessing epidemiological transitions, identifying emerging health threats, and monitoring progress toward national and global health goals, including the Sustainable Development Goals (SDGs); in particular, SDG Target 3.4 aims to reduce premature mortality from NCDs, while communicable diseases remain a key focus under Targets 3.3 and 3.8, which address disease-specific burdens and universal health coverage.\n\nInformation on the cause of death is especially important for low- and middle-income countries, where health systems must simultaneously address both infectious disease burdens and the rising prevalence of NCDs and injury-related mortality. Disaggregated cause-of-death data enable governments and development partners to prioritize interventions, assess health system performance, and guide investments in prevention and care. Moreover, monitoring mortality by cause helps identify disparities across population groups and geographies, ensuring that public health responses are equitable and data-driven.\n\n### Limitations and exceptions:\nLimited data availability on health status remains a major constraint in assessing health conditions in low-and middle-income countries. Surveillance systems are often weak or absent for major public health issues, and available estimates of disease prevalence and incidence may be incomplete or unreliable. National capacities and commitments to data collection and reporting vary significantly. To address these gaps and enhance reliability and comparability, the World Health Organization (WHO) produces estimates using epidemiological models.\n\nThe COVID-19 pandemic introduced exceptional challenges. Even in countries with relatively complete vital registration systems, excess mortality may have been misclassified between COVID-19 and other causes of death. In countries lacking comprehensive registration systems, greater reliance on modelled estimates\u2014including categories such as \u201cother pandemic-related mortality\u201d\u2014may have resulted in underestimation or redistribution of deaths from specific causes, particularly cardiovascular diseases and other non-communicable diseases.", "origins": [ { "producer": "World Health Organization Global Health Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021. Indicator SH.DTH.NCOM.ZS (https://data.worldbank.org/indicator/SH.DTH.NCOM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DTH.NCOM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sh_dth_nmrt": { "title": "Number of neonatal deaths", "description_from_producer": "Number of neonates dying before reaching 28 days of age.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis indicator is related to Sustainable Development Goal 3.2.2 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DTH.NMRT (https://data.worldbank.org/indicator/SH.DTH.NMRT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DTH.NMRT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dth_ocvd_zs": { "title": "Cause of death, by other COVID-19 pandemic-related outcomes (% of total)", "description_from_producer": "Cause of death refers to the share of all deaths for all ages by underlying causes. Other COVID-19 pandemic-related outcomes capture all deaths due to the pandemic which were not specifically caused by COVID-19 or the indirect COVID causes. These are estimated by subtracting COVID-19 specific deaths and deaths attributed to indirect COVID-19 causes from total excess mortality.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data on cause of death are compiled by the World Health Organization (WHO), using information from civil registration and vital statistics systems (CRVS), local health and demographic studies and other sources, supplemented by vital registration and verbal autopsy in communities as well as regular household health surveys. To address incomplete or inconsistent reporting\u2014particularly in countries with limited vital registration coverage\u2014WHO applies statistical models that account for underreporting, demographic differentials, and expected cause-of-death distribution. Special attention has also been paid to misattribution or miscoding of causes of death in cardiovascular diseases, cancer, injuries, and general ill-defined categories. For further information, refer to the WHO Global Health Estimates methodological documentation.\nStatistical concept(s): Causes of death in the Global Health Estimates (GHE) are classified according to the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) or ICD-9. Reported data from countries are mapped to a standardized cause list structured by WHO in a three-level hierarchy. Each death is assigned to a single underlying cause based on ICD rules. To ensure internal consistency, all cause-specific mortality estimates are constrained to fit within an all-cause mortality envelope derived from demographic estimates prepared by the UN Population Division and WHO. The cause list is designed to be mutually exclusive and collectively exhaustive, allowing full decomposition of total mortality by age, sex, country, and year.\n\nFor countries with incomplete, poor-quality, or no usable cause-of-death data, WHO uses a compositional cause modeling strategy. This approach estimates the distribution of causes within broad cause groups based on epidemiological and demographic covariates. Redistribution algorithms are applied to correct for misclassification or ill-defined causes. All modeling approaches are aligned with WHO\u2019s goal of maximizing comparability, transparency, and usability of mortality data across settings with diverse data availability and health system capacities.\n\n### Development relevance:\nUnderstanding the distribution of causes of death is critical for health planning, policy formulation, and the allocation of resources. Information on the cause of death\u2014 communicable diseases and maternal, prenatal and nutrition conditions, non-communicable diseases (NCDs), injury, and, in conjunction other COVID-19 pandemic-related outcomes\u2014provide a foundation for assessing epidemiological transitions, identifying emerging health threats, and monitoring progress toward national and global health goals, including the Sustainable Development Goals (SDGs); in particular, SDG Target 3.4 aims to reduce premature mortality from NCDs, while communicable diseases remain a key focus under Targets 3.3 and 3.8, which address disease-specific burdens and universal health coverage.\n\nInformation on the cause of death is especially important for low- and middle-income countries, where health systems must simultaneously address both infectious disease burdens and the rising prevalence of NCDs and injury-related mortality. Disaggregated cause-of-death data enable governments and development partners to prioritize interventions, assess health system performance, and guide investments in prevention and care. Moreover, monitoring mortality by cause helps identify disparities across population groups and geographies, ensuring that public health responses are equitable and data-driven.\n\n### Limitations and exceptions:\nLimited data availability on health status remains a major constraint in assessing health conditions in low-and middle-income countries. Surveillance systems are often weak or absent for major public health issues, and available estimates of disease prevalence and incidence may be incomplete or unreliable. National capacities and commitments to data collection and reporting vary significantly. To address these gaps and enhance reliability and comparability, the World Health Organization (WHO) produces estimates using epidemiological models.\n\nThe COVID-19 pandemic introduced exceptional challenges. Even in countries with relatively complete vital registration systems, excess mortality may have been misclassified between COVID-19 and other causes of death. In countries lacking comprehensive registration systems, greater reliance on modelled estimates\u2014including categories such as \u201cother pandemic-related mortality\u201d\u2014may have resulted in underestimation or redistribution of deaths from specific causes, particularly cardiovascular diseases and other non-communicable diseases.", "origins": [ { "producer": "World Health Organization Global Health Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Estimates, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death, note: Derived based on the data from Global Health Estimates Deaths by Cause, Age, Sex, by Country and by Region, 2000-2021. Indicator SH.DTH.OCVD.ZS (https://data.worldbank.org/indicator/SH.DTH.OCVD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DTH.OCVD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dyn_0509": { "title": "Probability of dying among children ages 5-9 years (per 1,000)", "description_from_producer": "Probability of dying between age 5-9 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DYN.0509 (https://data.worldbank.org/indicator/SH.DYN.0509). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.0509", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dyn_1014": { "title": "Probability of dying among adolescents ages 10-14 years (per 1,000)", "description_from_producer": "Probability of dying between age 10-14 years of age expressed per 1,000 adolescents age 10, if subject to age-specific mortality rates of the specified year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DYN.1014 (https://data.worldbank.org/indicator/SH.DYN.1014). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.1014", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dyn_1519": { "title": "Probability of dying among adolescents ages 15-19 years (per 1,000)", "description_from_producer": "Probability of dying between age 15-19 years of age expressed per 1,000 adolescents age 15, if subject to age-specific mortality rates of the specified year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DYN.1519 (https://data.worldbank.org/indicator/SH.DYN.1519). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.1519", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dyn_2024": { "title": "Probability of dying among youth ages 20-24 years (per 1,000)", "description_from_producer": "Probability of dying between age 20-24 years of age expressed per 1,000 youths age 20, if subject to age-specific mortality rates of the specified year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, adolescents, youth and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DYN.2024 (https://data.worldbank.org/indicator/SH.DYN.2024). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.2024", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dyn_aids_fe_zs": { "title": "Women's share of population ages 15+ living with HIV (%)", "description_from_producer": "Prevalence of HIV is the percentage of people who are infected with HIV. Female rate is as a percentage of the total population ages 15+ who are living with HIV.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: It is calculated as the number of women aged 15 and older living with HIV divided by the total number of people aged 15 and older living with HIV. Estimates of people living with HIV are produced by UNAIDS using a common modelling framework (Spectrum), which integrates country-reported HIV surveillance data, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators.\n\n\n\nReference: Annex 1. Methods for deriving UNAIDS HIV estimates, 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform:\n\nhttps://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nDespite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies.\n\n### Limitations and exceptions:\nThe limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information.", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.DYN.AIDS.FE.ZS (https://data.worldbank.org/indicator/SH.DYN.AIDS.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.AIDS.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sh_dyn_aids_zs": { "title": "Prevalence of HIV, total (% of population ages 15-49)", "description_from_producer": "Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\nStatistical concept(s): HIV prevalence rates reflect the rate of HIV infection in each country's population. Low national prevalence rates can be misleading, however. They often disguise epidemics that are initially concentrated in certain localities or population groups and threaten to spill over into the wider population. In many developing countries most new infections occur in young adults, with young women especially vulnerable.\n\n### Development relevance:\nDespite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS.\n\n### Limitations and exceptions:\nThe limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information.", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.DYN.AIDS.ZS (https://data.worldbank.org/indicator/SH.DYN.AIDS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.AIDS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population ages 15-49", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population ages 15-49" } }, "sh_dyn_mort": { "title": "Mortality rate, under-5 (per 1,000 live births)", "description_from_producer": "Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nGiven that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is the Sustainable Development Goal indicator 3.2.1[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org, publisher: UNICEF, WHO, World Bank, United Nations Population Division;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DYN.MORT (https://data.worldbank.org/indicator/SH.DYN.MORT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.MORT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 live births", "display": { "name": "Child mortality", "tolerance": 5, "isProjection": false, "conversionFactor": 0.1, "unit": "per 1,000 live births" }, "presentation": { "title_public": "Mortality rate, under-5 (per 1,000 live births)" } }, "sh_dyn_mort_fe": { "title": "Mortality rate, under-5, female (per 1,000 live births)", "description_from_producer": "Under-five mortality rate, female is the probability per 1,000 that a newborn female baby will die before reaching age five, if subject to female age-specific mortality rates of the specified year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nGiven that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DYN.MORT.FE (https://data.worldbank.org/indicator/SH.DYN.MORT.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.MORT.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 live births", "display": { "unit": "per 1,000 live births" } }, "sh_dyn_mort_ma": { "title": "Mortality rate, under-5, male (per 1,000 live births)", "description_from_producer": "Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nGiven that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.2.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DYN.MORT.MA (https://data.worldbank.org/indicator/SH.DYN.MORT.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.MORT.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 live births", "display": { "name": "Child mortality rate of boys", "tolerance": 5, "conversionFactor": 0.1, "unit": "per 1,000 live births" }, "presentation": { "title_public": "Mortality rate, under-5, male (per 1,000 live births)" } }, "sh_dyn_ncom_fe_zs": { "title": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, female (%)", "description_from_producer": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates.\n\n### Limitations and exceptions:\nDue to changes in input data and methods, the revisions of the data are not comparable to previously published estimates.\n\n\nThe current estimates for 2020 and 2021 are likely underestimated in countries where high-quality vital registration data was lacking at the time of GHE2021 production. This is because the modeled estimates cannot fully account for deaths from the four major NCDs indirectly attributed to the COVID-19 pandemic. Therefore, the data for 2020 and 2021 should be interpreted with caution.\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO), Global Health Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.DYN.NCOM.FE.ZS (https://data.worldbank.org/indicator/SH.DYN.NCOM.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.NCOM.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dyn_ncom_ma_zs": { "title": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70, male (%)", "description_from_producer": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates.\n\n### Limitations and exceptions:\nDue to changes in input data and methods, the revisions of the data are not comparable to previously published estimates.\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.4.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO), Global Health Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.DYN.NCOM.MA.ZS (https://data.worldbank.org/indicator/SH.DYN.NCOM.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.NCOM.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_dyn_ncom_zs": { "title": "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)", "description_from_producer": "Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The probability of death between the exact ages of 30 and 70 is calculated using cause-specific mortality rates for each 5-year age group, applying standard life table methods. The estimates are derived from the WHO Global Health Estimates (GHE). These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates.\n\n### Limitations and exceptions:\nDue to changes in input data and methods, the revisions of the data are not comparable to previously published estimates.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.4.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO), Global Health Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.DYN.NCOM.ZS (https://data.worldbank.org/indicator/SH.DYN.NCOM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.NCOM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sh_dyn_nmrt": { "title": "Mortality rate, neonatal (per 1,000 live births)", "description_from_producer": "Neonatal mortality rate is the number of neonates dying before reaching 28 days of age, per 1,000 live births in a given year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nGiven that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.\n\n\nThis is the Sustainable Development Goal indicator 3.2.2 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SH.DYN.NMRT (https://data.worldbank.org/indicator/SH.DYN.NMRT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.DYN.NMRT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 live births", "display": { "unit": "per 1,000 live births" } }, "sh_fpl_satm_zs": { "title": "Demand for family planning satisfied by modern methods (% of married women with demand for family planning)", "description_from_producer": "Demand for family planning satisfied by modern methods refers to the percentage of married women ages 15-49 years whose need for family planning is satisfied with modern methods.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated from nationally-representative household survey data. Relevant data for this indicator are collected through various multi-country survey programs, including Contraceptive Prevalence Surveys (CPS), Demographic and Health Surveys (DHS), Fertility and Family Surveys (FFS), Reproductive Health Surveys (RHS), Multiple Indicator Cluster Surveys (MICS), Performance Monitoring and Accountability 2020 surveys (PMA), World Fertility Surveys (WFS), other international survey programs, and national surveys.\n\n\n\n\n\nData compilation involves systematic searches of websites of international survey programs, survey databases (e.g., the Integrated Household Survey Network (IHSN) database), websites of national statistical offices, SDG national reporting platforms, and ad hoc queries. Additionally, country-specific information from UNFPA country offices is utilized.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.7.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Demographic and Health Surveys (DHS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys (DHS), uri: FP_NADM_W_PDM, date accessed: 2026-06-15. Indicator SH.FPL.SATM.ZS (https://data.worldbank.org/indicator/SH.FPL.SATM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.FPL.SATM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of married women with demand for family planning", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of married women with demand for family planning" } }, "sh_h2o_basw_ru_zs": { "title": "People using at least basic drinking water services, rural (% of rural population)", "description_from_producer": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the water service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services.\n\n### Development relevance:\nWater is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings.\n\n### Limitations and exceptions:\nNational, regional and income group estimates are made when data are available for at least 50 percent of the population.", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.H2O.BASW.RU.ZS (https://data.worldbank.org/indicator/SH.H2O.BASW.RU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.H2O.BASW.RU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of rural population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of rural population" } }, "sh_h2o_basw_ur_zs": { "title": "People using at least basic drinking water services, urban (% of urban population)", "description_from_producer": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the water service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services.\n\n### Development relevance:\nWater is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings.\n\n### Limitations and exceptions:\nNational, regional and income group estimates are made when data are available for at least 50 percent of the population.", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.H2O.BASW.UR.ZS (https://data.worldbank.org/indicator/SH.H2O.BASW.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.H2O.BASW.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of urban population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of urban population" } }, "sh_h2o_basw_zs": { "title": "People using at least basic drinking water services (% of population)", "description_from_producer": "The percentage of people using at least basic water services. This indicator encompasses both people using basic water services as well as those using safely managed water services. Basic drinking water services is defined as drinking water from an improved source, provided collection time is not more than 30 minutes for a round trip. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the water service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water). This indicator encompasses both people using basic water services as well as those using safely managed water services.\n\n### Development relevance:\nWater is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings.\n\n### Limitations and exceptions:\nNational, regional and income group estimates are made when data are available for at least 50 percent of the population.", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.H2O.BASW.ZS (https://data.worldbank.org/indicator/SH.H2O.BASW.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.H2O.BASW.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "sh_h2o_smdw_ru_zs": { "title": "People using safely managed drinking water services, rural (% of rural population)", "description_from_producer": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from fecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from fecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the water service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water).\n\n### Development relevance:\nWater is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings.\n\n### Limitations and exceptions:\nIn order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population.\n\n### Other notes:\nAggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.1.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme (JMP) for WASH and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.H2O.SMDW.RU.ZS (https://data.worldbank.org/indicator/SH.H2O.SMDW.RU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.H2O.SMDW.RU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of rural population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of rural population" } }, "sh_h2o_smdw_ur_zs": { "title": "People using safely managed drinking water services, urban (% of urban population)", "description_from_producer": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from fecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from fecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the water service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water).\n\n### Development relevance:\nWater is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings.\n\n### Limitations and exceptions:\nIn order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population.\n\n### Other notes:\nAggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.1.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme (JMP) for WASH and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.H2O.SMDW.UR.ZS (https://data.worldbank.org/indicator/SH.H2O.SMDW.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.H2O.SMDW.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of urban population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of urban population" } }, "sh_h2o_smdw_zs": { "title": "People using safely managed drinking water services (% of population)", "description_from_producer": "The percentage of people using drinking water from an improved source that is accessible on premises, available when needed and free from fecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for drinking water services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed drinking water services are defined as the water from an improved source that is accessible on premises, available when needed and free from fecal and priority chemical contamination. Improved water sources include piped water, boreholes or tubewells, protected dug wells, protected springs, and packaged or delivered water. The JMP classifies the water service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and surface water (Reference: https://washdata.org/monitoring/drinking-water).\n\n### Development relevance:\nWater is considered to be the most important resource for sustaining ecosystems, which provide life-supporting services for people, animals, and plants. Global access to safe water and proper hygiene education can reduce illness and death from disease, leading to improved health, poverty reduction, and socio-economic development. However, many countries are challenged to provide these basic necessities to their populations, leaving people at risk for water, sanitation, and hygiene (WASH)-related diseases. Because contaminated water is a major cause of illness and death, water quality is a determining factor in human poverty, education, and economic opportunities.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLack of access to adequate drinking water services contributes to deaths and illness, especially in children. Water based disease transmission by drinking contaminated water is responsible for significant outbreaks of diseases such as cholera and typhoid and includes diarrheal diseases, viral hepatitis A, cholera, dysentery and dracunculiasis (Guineaworm disease). Improving access to clean drinking water is a crucial element in the reduction of under-five mortality and morbidity and there is evidence that ensuring higher levels of drinking water services has a greater impact.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nWomen and children spend millions of hours each year fetching water. The chore diverts their time from other important activities (for example attending school, caring for children, participating in the economy). When water is not available on premises and has to be collected, women and girls are almost two and a half times more likely than men and boys to be the main water carriers for their families.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMany international organizations use access to safe drinking water and hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to safe drinking water is also considered to be a human right, not a privilege, for every man, woman, and child. Economic benefits of safe drinking water services include higher economic productivity, more education, and health-care savings.\n\n### Limitations and exceptions:\nIn order to meet the criteria for a safely managed drinking water service, an improved water source should meet three criteria: it should be accessible on the premises (accessibility), water should be available when needed (availability), and the water supplied should be free from contamination (quality). Many countries lack data on one or more elements of safely managed drinking water. The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) provide national estimates only when data are available on drinking water quality and at least one of the other criteria (accessibility and availability). Regional and income group estimates are made when data are available for at least 30 percent of the population.\n\n### Other notes:\nAggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\n\n\nThis indicator (Total) is calculated as a population-weighted average of the RURAL and URBAN aggregates when sufficient data are available for both domains. When coverage at either the RURAL or URBAN level is insufficient, but country-level TOTAL data meet the minimum population coverage threshold (30%), TOTAL aggregates are calculated directly from country-level TOTAL estimates. Because these two aggregation approaches may be applied in different years as data availability improves, methodological switches can occur and may result in discontinuities in the time series.\n\n\n\n\n\nThis indicator corresponds to Sustainable Development Goal indicator 6.1.1 (see UN SDG metadata: https://unstats.un.org/sdgs/metadata/).", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme (JMP) for WASH and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.H2O.SMDW.ZS (https://data.worldbank.org/indicator/SH.H2O.SMDW.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.H2O.SMDW.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "sh_hiv_0014": { "title": "Children (0-14) living with HIV", "description_from_producer": "Children living with HIV refers to the number of children ages 0-14 who are infected with HIV.\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nDespite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies.", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.0014 (https://data.worldbank.org/indicator/SH.HIV.0014). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.0014", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "0-14", "short_unit": "0-14", "display": { "shortUnit": "0-14", "unit": "0-14" } }, "sh_hiv_1524_fe_zs": { "title": "Prevalence of HIV, female (% ages 15-24)", "description_from_producer": "Prevalence of HIV, female is the percentage of females who are infected with HIV. Youth rates are as a percentage of the relevant age group.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nDespite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies.\n\n### Limitations and exceptions:\nThe limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information.\n\n### Other notes:\nIn many developing countries most new infections occur in young adults, with young women especially vulnerable.", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.1524.FE.ZS (https://data.worldbank.org/indicator/SH.HIV.1524.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.1524.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% ages 15-24", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% ages 15-24" } }, "sh_hiv_1524_ma_zs": { "title": "Prevalence of HIV, male (% ages 15-24)", "description_from_producer": "Prevalence of HIV, male is the percentage of males who are infected with HIV. Youth rates are as a percentage of the relevant age group.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nDespite the availability of effective treatment, HIV/AIDS remains a leading cause of death and a major global public health challenge. Low- and middle-income countries continue to bear a disproportionate share of the burden. Data on the number of people living with HIV, disaggregated by age and sex, are essential for understanding the populations most affected and for informing prevention, treatment, and care strategies.\n\n### Limitations and exceptions:\nThe limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information.\n\n### Other notes:\nIn many developing countries most new infections occur in young adults, with young women being especially vulnerable.", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.1524.MA.ZS (https://data.worldbank.org/indicator/SH.HIV.1524.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.1524.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% ages 15-24", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% ages 15-24" } }, "sh_hiv_artc_zs": { "title": "Antiretroviral therapy coverage (% of people living with HIV)", "description_from_producer": "Antiretroviral therapy coverage indicates the percentage of all people living with HIV who are receiving antiretroviral therapy.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nAntiretroviral therapy (ART) is central to the global HIV response and has significantly improved both survival and quality of life for people living with HIV. Effective ART suppresses viral load to undetectable levels, preventing progression to AIDS. When viral load remains undetectable, HIV is not sexually transmitted to HIV-negative partners.\n\nDespite this progress, gaps in treatment persist. Nearly 10 million people living with HIV are not receiving ART, and according to UNAIDS, about half of them reside in Africa. Expanding access to ART remains essential for reducing HIV-related morbidity and mortality and for achieving global targets for ending AIDS as a public health threat. (Reference: https://www.unaids.org/sites/default/files/2025-07/2025-global-aids-update-JC3153_en.pdf)\n\n### Limitations and exceptions:\nThe limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information.", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.ARTC.ZS (https://data.worldbank.org/indicator/SH.HIV.ARTC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.ARTC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of people living with HIV", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of people living with HIV" } }, "sh_hiv_incd": { "title": "Adults (ages 15-49) newly infected with HIV", "description_from_producer": "Number of adults (ages 15-49) newly infected with HIV.\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nDespite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission.\n\n### Other notes:\nThis indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.INCD (https://data.worldbank.org/indicator/SH.HIV.INCD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.INCD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "ages 15+", "short_unit": "ages 15+", "display": { "shortUnit": "ages 15+", "unit": "ages 15+" } }, "sh_hiv_incd_14": { "title": "Children (ages 0-14) newly infected with HIV", "description_from_producer": "Number of children (ages 0-14) newly infected with HIV.\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nDespite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission.\n\n### Other notes:\nThis indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.INCD.14 (https://data.worldbank.org/indicator/SH.HIV.INCD.14). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.INCD.14", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "ages 0-14", "display": { "unit": "ages 0-14" } }, "sh_hiv_incd_tl": { "title": "Adults (ages 15+) and children (ages 0-14) newly infected with HIV", "description_from_producer": "Number of adults (ages 15+) and children (ages 0-14) newly infected with HIV.\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nDespite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission.\n\n### Other notes:\nThis indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.INCD.TL (https://data.worldbank.org/indicator/SH.HIV.INCD.TL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.INCD.TL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "ages 0-14", "display": { "unit": "ages 0-14" } }, "sh_hiv_incd_tl_p3": { "title": "Incidence of HIV, all (per 1,000 uninfected population)", "description_from_producer": "Number of new HIV infections among uninfected populations expressed per 1,000 uninfected population in the year before the period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nDespite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.3.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.INCD.TL.P3 (https://data.worldbank.org/indicator/SH.HIV.INCD.TL.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.INCD.TL.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_hiv_incd_yg": { "title": "Young people (ages 15-24) newly infected with HIV", "description_from_producer": "Number of young people (ages 15-24) newly infected with HIV.\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nDespite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission.\n\n### Other notes:\nThis indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.INCD.YG (https://data.worldbank.org/indicator/SH.HIV.INCD.YG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.INCD.YG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_hiv_incd_yg_p3": { "title": "Incidence of HIV, ages 15-24 (per 1,000 uninfected population ages 15-24)", "description_from_producer": "Number of new HIV infections among uninfected populations ages 15-24 expressed per 1,000 uninfected population ages 15-24 in the year before the period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nDespite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission.\n\n### Other notes:\nThis is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.INCD.YG.P3 (https://data.worldbank.org/indicator/SH.HIV.INCD.YG.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.INCD.YG.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_hiv_incd_zs": { "title": "Incidence of HIV, ages 15-49 (per 1,000 uninfected population ages 15-49)", "description_from_producer": "Number of new HIV infections among uninfected populations ages 15-49 expressed per 1,000 uninfected population in the year before the period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: UNAIDS produces annual HIV/AIDS-related indicators using a common modelling framework (Spectrum). The model integrates country-reported data on HIV surveillance, antiretroviral therapy (ART) coverage, demographic information, and established patterns of HIV transmission, disease progression, and survival. Because many countries lack complete case reporting or cause-of-death data, these figures are modelled estimates and include uncertainty ranges. UNAIDS updates the estimates each year as new data and methods become available, ensuring internal consistency across all HIV/AIDS-related indicators. Reference: Annex 1 Methods for deriving UNAIDS HIV estimates \u2014 2025 Global AIDS Update \u2014 AIDS, Crisis and the Power to Transform: https://www.unaids.org/sites/default/files/2025-08/JC3153_GAU25_report_annex1_en.pdf\n\n### Development relevance:\nDespite the existence of effective medications and treatment, HIV/AIDS is still a leading cause of death and public health threat in the world. Low and middle income countries continue to bear a disproportionate share of the global burden of HIV/AIDS. The incidence rate provides a measure of progress toward preventing onward transmission of HIV. Also, the identification of newly infected persons will allow for interventions to reduce the risk of HIV transmission.\n\n### Other notes:\nThis is an age-disaggregated indicator for Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.INCD.ZS (https://data.worldbank.org/indicator/SH.HIV.INCD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.INCD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of uninfected population ages 15-49", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of uninfected population ages 15-49" } }, "sh_hiv_pmtc_zs": { "title": "Antiretroviral therapy coverage for PMTCT (% of pregnant women living with HIV)", "description_from_producer": "Percentage of pregnant women with HIV who receive antiretroviral medicine for prevention of mother-to-child transmission (PMTCT).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The coverage of antiretrovirals for PMTCT is calculated by dividing the number of pregnant women living with HIV who received antiretrovirals for PMTCT by the estimated number of pregnant women living with HIV who need antiretrovirals for PMTCT in the country.\n\n\n\n\nEstimating the Numerator: The number of pregnant women living with HIV receiving antiretrovirals for PMTCT is derived from national program data aggregated from facilities or other service delivery sites and reported by the country.\n\n\n\n\nEstimating the Denominator: The number of pregnant women living with HIV who need antiretroviral medicine for PMTCT is estimated using standardized statistical modeling based on UNAIDS/WHO methods. These methods consider various epidemic and demographic parameters, such as HIV prevalence among women of reproductive age, the effect of HIV on fertility, and national program coverage of antiretroviral therapy. These statistical modeling procedures provide a comprehensive population-based estimate of the number of pregnant women living with HIV who need antiretrovirals for PMTCT in the country.\n\n### Development relevance:\nHIV can be transmitted through sexual contact, blood transfusions, and the sharing of contaminated needles, and it can also be transmitted from mother to child during pregnancy, childbirth, or breastfeeding. Although the number of children acquiring HIV has decreased over time, mother-to-child transmission remains a significant public health concern.\n\nPrevention of mother-to-child transmission (PMTCT) is critical for reducing new pediatric HIV infections. However, many pregnant and breastfeeding women still do not begin ART or discontinue treatment during this period, contributing to continued transmission risks. Strengthening PMTCT services and ensuring continuity of care are essential for achieving global HIV prevention goals. (Reference: https://www.unaids.org/sites/default/files/2025-07/2025-global-aids-update-JC3153_en.pdf)", "origins": [ { "producer": "UNAIDS estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNAIDS estimates, Joint United Nations Programme on HIV/AIDS (UNAIDS), uri: https://aidsinfo.unaids.org/, publisher: UNAIDS, date accessed: 2025-08-27, date published: 2025-07. Indicator SH.HIV.PMTC.ZS (https://data.worldbank.org/indicator/SH.HIV.PMTC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.HIV.PMTC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of pregnant women living with HIV", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of pregnant women living with HIV" } }, "sh_imm_hepb": { "title": "Immunization, HepB3 (% of one-year-old children)", "description_from_producer": "Child immunization rate, hepatitis B is the percentage of children ages 12-23 months who received hepatitis B vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized after three doses.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package.\n\n### Development relevance:\nImmunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts.\n\n### Limitations and exceptions:\nIn many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics.", "origins": [ { "producer": "World Health Organization (WHO) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF). Indicator SH.IMM.HEPB (https://data.worldbank.org/indicator/SH.IMM.HEPB). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.IMM.HEPB", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of one-year-old children", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of one-year-old children" } }, "sh_imm_idpt": { "title": "Immunization, DPT (% of children ages 12-23 months)", "description_from_producer": "Child immunization, DPT, measures the percentage of children ages 12-23 months who received DPT vaccinations before 12 months or at any time before the survey. A child is considered adequately immunized against diphtheria, pertussis (or whooping cough), and tetanus (DPT) after receiving three doses of vaccine.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package.\n\n### Development relevance:\nImmunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts.\n\n### Limitations and exceptions:\nIn many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.b.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF). Indicator SH.IMM.IDPT (https://data.worldbank.org/indicator/SH.IMM.IDPT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.IMM.IDPT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children ages 12-23 months", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children ages 12-23 months" } }, "sh_imm_meas": { "title": "Immunization, measles (% of children ages 12-23 months)", "description_from_producer": "Child immunization, measles, measures the percentage of children ages 12-23 months who received the measles vaccination before 12 months or at any time before the survey. A child is considered adequately immunized against measles after receiving one dose of vaccine.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year. Notes on regional and global aggregates: When the vaccine is not introduced in a national immunization schedule, the missing value is assumed zero (or close to zero) in the relevant groups' averages.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package.\n\n### Development relevance:\nImmunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts.\n\n### Limitations and exceptions:\nIn many developing countries a lack of precise information on the size of the cohort of one-year-old children makes immunization coverage difficult to estimate from program statistics.", "origins": [ { "producer": "World Health Organization (WHO) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF), uri: https://data.unicef.org/topic/child-health/immunization/. Indicator SH.IMM.MEAS (https://data.worldbank.org/indicator/SH.IMM.MEAS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.IMM.MEAS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children ages 12-23 months", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children ages 12-23 months" } }, "sh_med_beds_zs": { "title": "Hospital beds (per 1,000 people)", "description_from_producer": "Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data were compiled from the WHO Regional offices and country sources other (e.g. ministry of health, national statistical office) and modified to standardize the unit of measure of per 10 000 population by WHO.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nAvailability and use of health services, such as hospital beds per 1,000 people, reflect both demand- and supply-side factors. In the absence of a consistent definition this is a crude indicator of the extent of physical, financial, and other barriers to health care.\n\n### Development relevance:\nHospital beds are used to indicate the availability of inpatient services.\n\n### Limitations and exceptions:\nDepending on the source and means of monitoring, data may not be exactly comparable across countries. For more information, see the original source.", "origins": [ { "producer": "World Health Organization (WHO) data, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO data, supplemented by country data, World Health Organization (WHO). Indicator SH.MED.BEDS.ZS (https://data.worldbank.org/indicator/SH.MED.BEDS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.MED.BEDS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 people", "display": { "unit": "per 1,000 people" } }, "sh_med_cmhw_p3": { "title": "Community health workers (per 1,000 people)", "description_from_producer": "Community health workers include various types of community health aides, many with country-specific occupational titles such as community health officers, community health-education workers, family health workers, lady health visitors and health extension package workers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The method of estimation for number of community health workers (including community health officers, community health-education workers, community health aides, family health workers and associated occupations) depends on the nature of the original data source. Enumeration based on population census data is a count of the number of people reporting 'community health worker' as their current occupation (as classified according to the tasks and duties of their job). A similar method is used for estimates based on labour force survey data, with the additional application of a sampling weight to calibrate for national representation. Data from health facility assessments and administrative reporting systems may be based on head counts of employees, staffing records, payroll records, training records, or tallies from other types of routine administrative records on human resources.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel.\n\n### Development relevance:\nThe WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006).\n\n### Limitations and exceptions:\nThe WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population.", "origins": [ { "producer": "World Health Organization (Global Health Workforce Statistics), OECD, and country data, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Workforce Statistics, World Health Organization (WHO);\nOrganisation for Economic Co-operation and Development (OECD);\nCountry data. Indicator SH.MED.CMHW.P3 (https://data.worldbank.org/indicator/SH.MED.CMHW.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.MED.CMHW.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 people", "display": { "unit": "per 1,000 people" } }, "sh_med_numw_p3": { "title": "Nurses and midwives (per 1,000 people)", "description_from_producer": "Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National focal points share the data with WHO through the online NHWA data platform. The platform hosted in WHO, is built to facilitate data reporting on the indicators listed in the NHWA Handbook and data sharing across all the 3 levels of WHO.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel.\n\n### Development relevance:\nThe WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006).\n\n### Limitations and exceptions:\nThe WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (Global Health Workforce Statistics), OECD, and country data, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Workforce Statistics, World Health Organization (WHO);\nOrganisation for Economic Co-operation and Development (OECD);\nCountry data. Indicator SH.MED.NUMW.P3 (https://data.worldbank.org/indicator/SH.MED.NUMW.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.MED.NUMW.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 people", "display": { "unit": "per 1,000 people" } }, "sh_med_phys_zs": { "title": "Physicians (per 1,000 people)", "description_from_producer": "Physicians include generalist and specialist medical practitioners.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National focal points share the data with WHO through the online NHWA data platform. The platform hosted in WHO, is built to facilitate data reporting on the indicators listed in the NHWA Handbook and data sharing across all the 3 levels of WHO.\nStatistical concept(s): Health systems - the combined arrangements of institutions and actions whose primary purpose is to promote, restore, or maintain health (World Health Organization, World Health Report 2000) - are increasingly being recognized as key to combating disease and improving the health status of populations. The World Bank's Healthy Development: Strategy for Health, Nutrition, and Population Results emphasizes the need to strengthen health systems, which are weak in many countries, in order to increase the effectiveness of programs aimed at reducing specific diseases and further reduce morbidity and mortality. To evaluate health systems, the World Health Organization (WHO) has recommended that key components - such as financing, service delivery, workforce, governance, and information - be monitored using several key indicators. The data are a subset of the key indicators. Monitoring health systems allows the effectiveness, efficiency, and equity of different health system models to be compared. Health system data also help identify weaknesses and strengths and areas that need investment, such as additional health facilities, better health information systems, or better trained human resources.\n\n\n\nData on health worker (physicians, nurses and midwives, and community health workers) density show the availability of medical personnel.\n\n### Development relevance:\nThe WHO estimates that at least 2.5 medical staff (physicians, nurses and midwives) per 1,000 people are needed to provide adequate coverage with primary care interventions (WHO, World Health Report 2006).\n\n### Limitations and exceptions:\nThe WHO compiles data from household and labor force surveys, censuses, and administrative records. Data comparability is limited by differences in definitions and training of medical personnel varies. In addition, human resources tend to be concentrated in urban areas, so that average densities do not provide a full picture of health personnel available to the entire population.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (Global Health Workforce Statistics), OECD, and country data, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Workforce Statistics, World Health Organization (WHO);\nOrganisation for Economic Co-operation and Development (OECD);\nCountry data. Indicator SH.MED.PHYS.ZS (https://data.worldbank.org/indicator/SH.MED.PHYS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.MED.PHYS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 people", "display": { "unit": "per 1,000 people" } }, "sh_med_saop_p5": { "title": "Specialist surgical workforce (per 100,000 population)", "description_from_producer": "Specialist surgical workforce is the number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100,000 population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100 000 population.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country\u2019s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery.\n\n### Development relevance:\nBillions of people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked.\n\n### Limitations and exceptions:\nPrior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that \"We can't manage what we don't measure\", the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data shows large gaps in SAO care across countries by income groups.", "origins": [ { "producer": "Lancet Commission on Global Surgery, WHO, and BMJ Global Health, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Lancet Commission on Global Surgery, uri: www.lancetglobalsurgery.org, note: Data collected by the Lancet Commission on Global Surgery;\nWHO Collaborating Centre for Surgery and Public Health, note: data collected by WHO Collaborating Centre for Surgery and Public Health at Lund University from various sources including Ministries of Health or equivalent national regulatory bodies, national official entities such as medical councils, Eurostat, OECD, WHO Euro Health For All Database, WHO EURO Technical resources for health Database;\nBMJ Glob Health. Indicator SH.MED.SAOP.P5 (https://data.worldbank.org/indicator/SH.MED.SAOP.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.MED.SAOP.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 population", "display": { "unit": "per 100,000 population" } }, "sh_mlr_incd_p3": { "title": "Incidence of malaria (per 1,000 population at risk)", "description_from_producer": "Incidence of malaria is the number of new cases of malaria in a year per 1,000 population at risk.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Confirmed malaria cases for countries and areas outside Africa, and for low-transmission countries and areas in Africa are adjusted for extent of health service use (treatment seeking), underreporting and lack of case confirmation (the likelihood that cases are parasite positive). In high transmission areas in which the quality of surveillance data does not permit a robust estimate from the number of reported cases, but good data on parasite prevalence is available, the number of cases can be estimated from parasite prevalence. The denominator is estimated, using official UN population and population at risk estimates for countries with sub-national endemicity.\nStatistical concept(s): Complete data on malaria cases reported through surveillance systems are the best source of data but are rarely available for large populations at high quality and accuracy. Reported data on malaria cases generally need to be adjusted for extent of health service use (treatment seeking), underreporting and lack of case confirmation (the likelihood that cases are parasite positive). WHO compiles data on reported confirmed cases of malaria and suspected cases tested with microscopy or RDT, submitted by national malaria control programmes. Underreporting is reported or estimated by countries. The extent of health service use (treatment seeking) data were obtained from nationally representative household surveys on health service use.\n\n### Development relevance:\nMalaria is a life-threatening disease caused by parasites that are transmitted to people through the bites of infected female Anopheles mosquitoes. It is preventable and curable. There are 5 parasite species that cause malaria in humans, and 2 of these species \u2013 Plasmodium falciparum and Plasmodium vivax \u2013 pose the greatest threat.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.3.3[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (Global Health Observatory), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO), uri: http://apps.who.int/ghodata/. Indicator SH.MLR.INCD.P3 (https://data.worldbank.org/indicator/SH.MLR.INCD.P3). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.MLR.INCD.P3", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 population at risk", "display": { "unit": "per 1,000 population at risk" } }, "sh_mlr_nets_zs": { "title": "Use of insecticide-treated bed nets (% of under-5 population)", "description_from_producer": "Use of insecticide-treated bed nets refers to the percentage of children under age five who slept under an insecticide-treated bednet to prevent malaria.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Malaria is endemic to the poorest countries in the world, mainly in tropical and subtropical regions of Africa, Asia, and the Americas. Insecticide-treated nets, properly used and maintained, are one of the most important malaria-preventive strategies to limit human-mosquito contact.", "origins": [ { "producer": "Demographic and Health Surveys (DHS) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, Demographic and Health Surveys (DHS) Program. Indicator SH.MLR.NETS.ZS (https://data.worldbank.org/indicator/SH.MLR.NETS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.MLR.NETS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of under-5 population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of under-5 population" } }, "sh_mlr_tret_zs": { "title": "Children with fever receiving antimalarial drugs (% of children under age 5 with fever)", "description_from_producer": "Malaria treatment refers to the percentage of children under age five who were ill with fever in the last two weeks and received any appropriate (locally defined) anti-malarial drugs.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Malaria is endemic to the poorest countries in the world, mainly in tropical and subtropical regions of Africa, Asia, and the Americas. Prompt and effective treatment of malaria is a critical element of malaria control. It is vital that sufferers, especially children under age 5, start treatment within 24 hours of the onset of symptoms, to prevent progression - often rapid - to severe malaria and death. Data on malaria are from national-level surveys, including Multiple Indicator Cluster Surveys, Demographic and Health Surveys, and Malaria Indicator Surveys.", "origins": [ { "producer": "Demographic and Health Surveys (DHS) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, Demographic and Health Surveys (DHS) Program. Indicator SH.MLR.TRET.ZS (https://data.worldbank.org/indicator/SH.MLR.TRET.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.MLR.TRET.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under age 5 with fever", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under age 5 with fever" } }, "sh_mmr_dths": { "title": "Number of maternal deaths", "description_from_producer": "A maternal death refers to the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management but not from accidental or incidental causes.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.\n\n### Development relevance:\nChildbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants.\n\n### Limitations and exceptions:\nThe methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The figures cannot be assumed to provide exact estimates.", "origins": [ { "producer": "World Health Organization (WHO), UNICEF, UNFPA, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality. Indicator SH.MMR.DTHS (https://data.worldbank.org/indicator/SH.MMR.DTHS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.MMR.DTHS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_mmr_risk": { "title": "Lifetime risk of maternal death (1 in: rate varies by country)", "description_from_producer": "Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.\n\n### Development relevance:\nChildbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants.\n\n### Limitations and exceptions:\nThe methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death.", "origins": [ { "producer": "World Health Organization (WHO), UNICEF, UNFPA, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality. Indicator SH.MMR.RISK (https://data.worldbank.org/indicator/SH.MMR.RISK). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.MMR.RISK", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "1 in: rate varies by country", "display": { "unit": "1 in: rate varies by country" } }, "sh_mmr_risk_zs": { "title": "Lifetime risk of maternal death (%)", "description_from_producer": "Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertility and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.\n\n### Development relevance:\nChildbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants.\n\n### Limitations and exceptions:\nThe methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The probability cannot be assumed to provide an exact estimate of risk of maternal death.", "origins": [ { "producer": "World Health Organization (WHO), UNICEF, UNFPA, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Trends in Maternal Mortality, World Health Organization (WHO);\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality. Indicator SH.MMR.RISK.ZS (https://data.worldbank.org/indicator/SH.MMR.RISK.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.MMR.RISK.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sh_prg_anem": { "title": "Prevalence of anemia among pregnant women (%)", "description_from_producer": "Prevalence of anemia, pregnant women, is the percentage of pregnant women whose hemoglobin level is less than 110 grams per liter at sea level.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Anemia is a condition in which the number of red blood cells or their oxygen-carrying capacity is insufficient to meet physiologic needs, which vary by age, sex, altitude, smoking status, and pregnancy status. In its severe form it is associated with fatigue, weakness, dizziness, and drowsiness. Children under age 5 and pregnant women have the highest risk for anemia.\n\n### Limitations and exceptions:\nData should be used with caution because surveys differ in quality, coverage, age group interviewed, and treatment of missing values across countries and over time.\n\n\n\nData on anemia are compiled by the WHO based mainly on nationally representative surveys, which measure hemoglobin in the blood. WHO's hemoglobin thresholds are then used to determine anemia status based on age, sex, and physiological status.", "origins": [ { "producer": "Global Health Observatory / World Health Statistics - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO). Indicator SH.PRG.ANEM (https://data.worldbank.org/indicator/SH.PRG.ANEM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.PRG.ANEM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sh_prv_smok": { "title": "Prevalence of current tobacco use (% of adults)", "description_from_producer": "The percentage of the population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), \u201ce-cigars\u201d, \u201ce-hookahs\u201d, JUUL and \u201ce-pipes\u201d. The rates are age-standardized to the WHO Standard Population.\n\n### Periodicity:\nBiennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nA statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women.\n\n\n\nThe model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate.\n\nDepending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is \u201cborrowed\u201d from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966\u2013976 (2015).\n\n### Limitations and exceptions:\nEstimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, total (ages 15+)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes. The indicator name and definition were updated in December, 2020.", "origins": [ { "producer": "Global Health Observatory Data Repository - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.PRV.SMOK (https://data.worldbank.org/indicator/SH.PRV.SMOK). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.PRV.SMOK", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of adults", "short_unit": "%", "display": { "name": "Adults who smoke", "shortUnit": "%", "unit": "% of adults" }, "presentation": { "title_public": "Prevalence of current tobacco use (% of adults)" } }, "sh_prv_smok_fe": { "title": "Prevalence of current tobacco use, females (% of female adults)", "description_from_producer": "The percentage of the female population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), \u201ce-cigars\u201d, \u201ce-hookahs\u201d, JUUL and \u201ce-pipes\u201d. The rates are age-standardized to the WHO Standard Population.\n\n### Periodicity:\nBiennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nA statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women.\n\n\n\nThe model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate.\n\nDepending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is \u201cborrowed\u201d from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966\u2013976 (2015).\n\n### Limitations and exceptions:\nEstimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, females (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020.", "origins": [ { "producer": "Global Health Observatory Data Repository - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.PRV.SMOK.FE (https://data.worldbank.org/indicator/SH.PRV.SMOK.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.PRV.SMOK.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of adults", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of adults" } }, "sh_prv_smok_ma": { "title": "Prevalence of current tobacco use, males (% of male adults)", "description_from_producer": "The percentage of the male population ages 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis. Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), \u201ce-cigars\u201d, \u201ce-hookahs\u201d, JUUL and \u201ce-pipes\u201d. The rates are age-standardized to the WHO Standard Population.\n\n### Periodicity:\nBiennial\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nSmoking is the most common form of tobacco use and the prevalence of smoking is therefore a good measure of the tobacco epidemic. (Corrao MA, Guindon GE, Sharma N, Shokoohi DF (eds). Tobacco Control Country Profiles, 2000, American Cancer Society, Atlanta.) Tobacco use causes heart and other vascular diseases and cancers of the lung and other organs. Given the long delay between starting to smoke and the onset of disease, the health impact of smoking will increase rapidly only in the next few decades. The data presented are age-standardized rates for adults ages 15 and older from the WHO.\n\n### Limitations and exceptions:\nEstimates for countries with irregular surveys or many data gaps have large uncertainty ranges, and such results should be interpreted with caution.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.a.1 [https://unstats.un.org/sdgs/metadata/].\n\nPrevious indicator name: Smoking prevalence, males (% of adults)\nThe previous indicator excluded smokeless tobacco use, while the current indicator includes it. The indicator name and definition were updated in December, 2020.", "origins": [ { "producer": "Global Health Observatory Data Repository - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.PRV.SMOK.MA (https://data.worldbank.org/indicator/SH.PRV.SMOK.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.PRV.SMOK.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of adults", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of adults" } }, "sh_sgr_crsk_zs": { "title": "Risk of catastrophic expenditure for surgical care (% of people at risk)", "description_from_producer": "The proportion of population at risk of catastrophic expenditure when surgical care is required. Catastrophic expenditure is defined as direct out of pocket payments for surgical and anaesthesia care exceeding 10% of total income.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The probability of experiencing catastrophic expenditure (10 percent of total income) when surgical care is required.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country\u2019s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery.\n\n### Development relevance:\nBillions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked.\n\n### Limitations and exceptions:\nPrior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that \u201cWe can\u2019t manage what we don\u2019t measure\u201d, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups.", "origins": [ { "producer": "Harvard Medical School Program in Global Surgery and Social Change (PGSSC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Program in Global Surgery and Social Change (PGSSC), Harvard Medical School, uri: https://www.pgssc.org/. Indicator SH.SGR.CRSK.ZS (https://data.worldbank.org/indicator/SH.SGR.CRSK.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.SGR.CRSK.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of people at risk", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of people at risk" } }, "sh_sgr_irsk_zs": { "title": "Risk of impoverishing expenditure for surgical care (% of people at risk)", "description_from_producer": "The proportion of population at risk of impoverishing expenditure when surgical care is required. Impoverishing expenditure is defined as direct out of pocket payments for surgical and anaesthesia care which drive people below a poverty threshold (using a threshold of $2.15 PPP/day).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The probability of experiencing impoverishment when surgical care is required.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country\u2019s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery.\n\n### Development relevance:\nBillions people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked.\n\n### Limitations and exceptions:\nPrior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that \u201cWe can\u2019t manage what we don\u2019t measure\u201d, the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data show large gaps in SAO care across countries by income groups.", "origins": [ { "producer": "Harvard Medical School Program in Global Surgery and Social Change (PGSSC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Program in Global Surgery and Social Change (PGSSC), Harvard Medical School, uri: https://www.pgssc.org/. Indicator SH.SGR.IRSK.ZS (https://data.worldbank.org/indicator/SH.SGR.IRSK.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.SGR.IRSK.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of people at risk", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of people at risk" } }, "sh_sgr_proc_p5": { "title": "Number of surgical procedures (per 100,000 population)", "description_from_producer": "The number of procedures undertaken in an operating theatre per 100,000 population per year in each country. A procedure is defined as the incision, excision, or manipulation of tissue that needs regional or general anaesthesia, or profound sedation to control pain.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The number of procedures undertaken in an operating theatre per 100 000 population per year in each country. A procedure is defined as the incision, excision, or manipulation of tissue that needs regional or general anaesthesia, or profound sedation to control pain.\nStatistical concept(s): The Lancet Commission on Global Surgery, assembled in 2013 to assess surgical care around the world. Commissioners engaged in an iterative global consultative process with partners in over 110 countries to develop six core indicators of the strength of a surgical system. Two indicators assess a country\u2019s preparedness to deliver safe surgery and anesthesia, two assess the current delivery of safe care, and two assess the state of financial risk protection for those seeking surgery.\n\n### Development relevance:\nBillions of people lack access to safe and affordable surgical, anesthesia and obstetric (SAO) care while a third of the global burden of disease requires surgical and/or anesthesia decision-making or treatment. Treating the sick very often requires surgery and anesthesia. Despite such huge burden of disease, safe and affordable SAO care is often overlooked.\n\n### Limitations and exceptions:\nPrior to 2015, global data on surgery, anesthesia and obstetric care was virtually nonexistent. With the idea that \"We can't manage what we don't measure\", the Lancet Commission on Global Surgery developed six Surgical, Obstetric and Anesthesia (SAO) indicators and collected data for them. The analysis of these data shows large gaps in SAO care across countries by income groups.", "origins": [ { "producer": "Lancet Commission on Global Surgery and the Center for Health Equity in Surgery and Anesthesia (UCSF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Lancet Commission on Global Surgery, uri: www.lancetglobalsurgery.org, note: Data from various sources compiled by the Lancet Commission on Global Surgery and the Center for Health Equity in Surgery and Anesthesia at UCSF Medical Center;\nCenter for Health Equity in Surgery and Anesthesia, note: Data from various sources compiled by the Lancet Commission on Global Surgery and the Center for Health Equity in Surgery and Anesthesia at UCSF Medical Center. Indicator SH.SGR.PROC.P5 (https://data.worldbank.org/indicator/SH.SGR.PROC.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.SGR.PROC.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 population", "display": { "unit": "per 100,000 population" } }, "sh_sta_airp_fe_p5": { "title": "Mortality rate attributed to household and ambient air pollution, age-standardized, female (per 100,000 female population)", "description_from_producer": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003).\n\n### Development relevance:\nAir pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries.\n\n### Limitations and exceptions:\nEstimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Global Health Observatory Data Repository - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.STA.AIRP.FE.P5 (https://data.worldbank.org/indicator/SH.STA.AIRP.FE.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.AIRP.FE.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_sta_airp_ma_p5": { "title": "Mortality rate attributed to household and ambient air pollution, age-standardized, male (per 100,000 male population)", "description_from_producer": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003).\n\n### Development relevance:\nAir pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries.\n\n### Limitations and exceptions:\nEstimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.9.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Global Health Observatory Data Repository - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.STA.AIRP.MA.P5 (https://data.worldbank.org/indicator/SH.STA.AIRP.MA.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.AIRP.MA.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_sta_airp_p5": { "title": "Mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population)", "description_from_producer": "Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Burden of disease (or in the present case attributable mortality) is calculated by first combining information on the increased (or relative) risk of a disease resulting from exposure, with information on how widespread the exposure is in the population (e.g. the annual mean concentration of particulate matter to which the population is exposed). This allows calculation of the 'population attributable fraction' (PAF), which is the fraction of disease seen in a given population that can be attributed to the exposure (e.g in this case the annual mean concentration of particulate matter). Applying this fraction to the total burden of disease (e.g. cardiopulmonary disease expressed as deaths or DALYs), gives the total number of deaths or DALYs that results from exposure to that particular risk factor (in the example given above, to ambient air pollution). To estimate the combined effects of risk factors, a joint population attributable fraction is calculated, as described in Ezzati et al (2003).\n\n### Development relevance:\nAir pollution is one of the biggest environmental risks to health. According to the World Health Organization, the combined effects of ambient (outdoor) and household air pollution cause about 7 million premature deaths every year. Most deaths occur due to increased mortality from stroke, heart disease, chronic obstructive pulmonary disease, lung cancer and acute respiratory infections. The majority of the burden is borne by populations in low and middle income countries.\n\n### Limitations and exceptions:\nEstimates of the joint effects of air pollution are constrained by limited knowledge on the distribution of the population exposed to both household and ambient air pollution, correlation of exposures at individual level as household air pollution is a contributor to ambient air pollution, and non-linear interactions\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.9.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Global Health Observatory Data Repository - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository, World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.STA.AIRP.P5 (https://data.worldbank.org/indicator/SH.STA.AIRP.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.AIRP.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 population", "display": { "unit": "per 100,000 population" } }, "sh_sta_anvc_zs": { "title": "Pregnant women receiving prenatal care (%)", "description_from_producer": "Pregnant women receiving prenatal care are the percentage of women attended at least once during pregnancy by skilled health personnel for reasons related to pregnancy.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nGood prenatal and postnatal care improves maternal health and reduces maternal and infant mortality. However, indicators on use of antenatal care services provide no information on the content or quality of the services. Data on antenatal care are obtained mostly from household surveys, which ask women who have had a live birth whether and from whom they received antenatal care.\n\n### Limitations and exceptions:\nFor the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources.\n\n### Other notes:\nGood prenatal and postnatal care improve maternal health and reduce maternal and infant mortality.", "origins": [ { "producer": "Demographic and Health Surveys (DHS) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, Demographic and Health Surveys (DHS) Program. Indicator SH.STA.ANVC.ZS (https://data.worldbank.org/indicator/SH.STA.ANVC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.ANVC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sh_sta_aric_zs": { "title": "ARI treatment (% of children under 5 taken to a health provider)", "description_from_producer": "Children with acute respiratory infection (ARI) who are taken to a health provider refers to the percentage of children under age five with ARI in the last two weeks who were taken to an appropriate health provider, including hospital, health center, dispensary, village health worker, clinic, and private physician.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Acute respiratory infection continues to be a leading cause of death among young children. Data are drawn mostly from household health surveys in which mothers report on number of episodes and treatment for acute respiratory infection.", "origins": [ { "producer": "Demographic and Health Surveys (DHS) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, Demographic and Health Surveys (DHS) Program. Indicator SH.STA.ARIC.ZS (https://data.worldbank.org/indicator/SH.STA.ARIC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.ARIC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5 taken to a health provider", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5 taken to a health provider" } }, "sh_sta_bass_ru_zs": { "title": "People using at least basic sanitation services, rural (% of rural population)", "description_from_producer": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, composting toilets or pit latrines with slabs.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services.\n\n### Development relevance:\nSanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired.\n\n### Limitations and exceptions:\nNational, regional and income group estimates are made when data are available for at least 50 percent of the population.", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.BASS.RU.ZS (https://data.worldbank.org/indicator/SH.STA.BASS.RU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.BASS.RU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of rural population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of rural population" } }, "sh_sta_bass_ur_zs": { "title": "People using at least basic sanitation services, urban (% of urban population)", "description_from_producer": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, composting toilets or pit latrines with slabs.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services.\n\n### Development relevance:\nSanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired.\n\n### Limitations and exceptions:\nNational, regional and income group estimates are made when data are available for at least 50 percent of the population.", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.BASS.UR.ZS (https://data.worldbank.org/indicator/SH.STA.BASS.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.BASS.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of urban population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of urban population" } }, "sh_sta_bass_zs": { "title": "People using at least basic sanitation services (% of population)", "description_from_producer": "The percentage of people using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households. This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines; ventilated improved pit latrines, composting toilets or pit latrines with slabs.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation). This indicator encompasses both people using basic sanitation services as well as those using safely managed sanitation services.\n\n### Development relevance:\nSanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired.\n\n### Limitations and exceptions:\nNational, regional and income group estimates are made when data are available for at least 50 percent of the population.", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.BASS.ZS (https://data.worldbank.org/indicator/SH.STA.BASS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.BASS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "sh_sta_bfed_zs": { "title": "Exclusive breastfeeding (% of children under 6 months)", "description_from_producer": "Exclusive breastfeeding refers to the percentage of children less than six months old who are fed breast milk alone (no other liquids) in the past 24 hours.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by dividing the number of infants aged 0\u20135 months who received only breast milk during the previous day by the total number of infants aged 0\u20135 months, then multiplying the result by 100.\n\n\nData collection involves Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), which include questions about liquids and foods given the previous day, as well as the number of milk feeds the previous day, to determine if the child is being exclusively breastfed. WHO and UNICEF jointly collect data on infant and young child feeding, pooling information from national surveys. Additionally, the WHO Programme of Nutrition, Physical Activity, and Obesity at the Regional Office for Europe independently compiles country-specific information on exclusive breastfeeding.\n\n### Development relevance:\nFor optimal infant and young child feeding, mothers initiate breastfeeding within one hour of birth, breastfeed exclusively for the first six months, and continue to breastfeed for two years or more while providing nutritionally adequate, safe, and age-appropriate solid, semisolid, and soft foods. Breast milk alone contains all the nutrients, antibodies, hormones, and antioxidants an infant needs to thrive. It protects babies from diarrhea and acute respiratory infections, stimulates their immune systems and response to vaccination, and may confer cognitive benefits.\n\n### Limitations and exceptions:\nMost of the data on breastfeeding are derived from household surveys. For the data that are from household surveys, the year refers to the survey year.", "origins": [ { "producer": "Demographic and Health Surveys (DHS) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, Demographic and Health Surveys (DHS) Program. Indicator SH.STA.BFED.ZS (https://data.worldbank.org/indicator/SH.STA.BFED.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.BFED.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 6 months", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 6 months" } }, "sh_sta_brtc_zs": { "title": "Births attended by skilled health staff (% of total)", "description_from_producer": "Births attended by skilled health staff are the percentage of deliveries attended by personnel trained to give the necessary supervision, care, and advice to women during pregnancy, labor, and the postpartum period; to conduct deliveries on their own; and to care for newborns.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: National-level household surveys are the primary sources for collecting data on skilled health personnel providing childbirth care. These surveys include Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Reproductive Health Surveys (RHS), and other national surveys based on similar methodologies. Respondents in these surveys are asked about their last live birth and who assisted during delivery, covering a period of up to five years before the interview.\n\n\nAs part of the data harmonization process and interaction with countries, UNICEF conducts an annual country consultation. During this consultation, SDG country focal points are contacted to update and verify values included in the database and to obtain new data sources. These new data sources are reviewed and assessed jointly with WHO. Additionally, the national categories or occupational titles of skilled health personnel are verified. The reported data for some countries may include additional categories of trained personnel beyond doctors, nurses, and midwives.\n\n### Development relevance:\nReproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\n\n\nThe share of births attended by skilled health staff is an indicator of a health system's ability to provide adequate care for pregnant women.\n\n### Limitations and exceptions:\nFor the indicators that are from household surveys, the year refers to the survey year. For more information, consult the original sources.\n\n### Other notes:\nAssistance by trained professionals during birth reduces the incidence of maternal deaths during childbirth. The share of births attended by skilled health staff is an indicator of a health system\u2019s ability to provide adequate care for pregnant women.\n\nThis is the Sustainable Development Goal indicator 3.1.2[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Demographic and Health Surveys (DHS) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, Demographic and Health Surveys (DHS) Program. Indicator SH.STA.BRTC.ZS (https://data.worldbank.org/indicator/SH.STA.BRTC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.BRTC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sh_sta_brtw_zs": { "title": "Low-birthweight babies (% of births)", "description_from_producer": "Low-birthweight babies are newborns weighing less than 2,500 grams, with the measurement taken within the first hour of life, before significant postnatal weight loss has occurred.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Household Surveys including DHS, MICS and other national surveys\nStatistical concept(s): Low birthweight babies are more likely to die during their first month of life and those who survived face lifelong consequences including a higher risk of stunted growth, lower IQ ,and adult-onset chronic conditions such as obesity and diabetes.\n\n### Development relevance:\nLow birth-weight, which is associated with maternal malnutrition, raises the risk of infant mortality and stunts growth in infancy and childhood. There is also emerging evidence that low-birth-weight babies are more prone to non-communicable diseases such as diabetes and cardiovascular diseases. Low birth-weight can arise as a result of a baby being born too soon or too small for gestational age. Babies born prematurely, who are also small for their gestational age, have the worst prognosis.\n\n\n\nIn low- and middle-income countries low birth-weight stems primarily from poor maternal health and nutrition. Three factors have the most impact: poor maternal nutritional status before conception, mother's short stature (due mostly to under-nutrition and infections during childhood), and poor nutrition during pregnancy (UNICEF Data, https://data.unicef.org/).", "origins": [ { "producer": "UNICEF and World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF-WHO Low birthweight estimates, UN Children's Fund (UNICEF), uri: data.unicef.org;\nWorld Health Organization (WHO), uri: data.unicef.org, note: UNICEF-WHO Low birthweight estimates. Indicator SH.STA.BRTW.ZS (https://data.worldbank.org/indicator/SH.STA.BRTW.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.BRTW.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of births", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of births" } }, "sh_sta_diab_zs": { "title": "Diabetes prevalence (% of population ages 20 to 79)", "description_from_producer": "Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data used to estimate diabetes prevalence were gathered from various sources. Most of the data were extracted from peer-reviewed publications and national health surveys, including selected WHO STEPwise approach to surveillance (WHO STEPS) studies. Additionally, data from other official sources, such as registries and reports from health regulatory bodies, were utilized, provided there was sufficient information to assess their quality. Data sources with adequate methodological information on key areas of interest, such as the method of diagnosis and sample representativeness, were included. Given the significance of age as a major determinant for diabetes prevalence, only studies with at least three age-specific estimates were considered. After selecting the data sources, the reported age- and sex-specific data in each source were smoothed using a logistic regression model.\n\n### Development relevance:\nDiabetes, an important cause of ill health and a risk factor for other diseases in developed countries, is spreading rapidly in developing countries. Highest among the elderly, prevalence rates are rising among younger and productive populations in developing countries. Economic development has led to the spread of Western lifestyles and diet to developing countries, resulting in a substantial increase in diabetes. Without effective prevention and control programs, diabetes will likely continue to increase.\n\n### Limitations and exceptions:\nThe limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information.", "origins": [ { "producer": "International Diabetes Federation (Diabetes Atlas), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Diabetes Atlas, International Diabetes Federation. Indicator SH.STA.DIAB.ZS (https://data.worldbank.org/indicator/SH.STA.DIAB.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.DIAB.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population ages 20 to 79", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population ages 20 to 79" } }, "sh_sta_fgms_zs": { "title": "Female genital mutilation prevalence (%)", "description_from_producer": "Percentage of women aged 15\u201349 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons.\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated by the number of women ages 15-49 who have undergone FGM divided by the total number of women ages 15-49 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). The majority of the data are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): Female genital mutilation (FGM) encompasses all practices that involve the partial or total removal of the external female genitalia, or other injury to the female genital organs, for non-medical reasons. Typically, this procedure is carried out on minors.\n\n### Development relevance:\nFGM is a harmful practice involving the cutting or removal of the external female genitalia. It does not have any health benefits but rather causes serious risks to women\u2019s physical and psychological health, including chronic infections, pain, menstrual problems, and complications during childbirth. FGM has been practiced mainly in the western, eastern, and north-eastern regions of Africa and some countries in the Middle East and Asia. It is reported that FGM is also found in western countries such as United Kingdom, United States, and Canada.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nFGM is a violation of girls\u2019 and women\u2019s human rights, as well as a violation of women\u2019s rights to health, security, and physical integrity. However, its eradication is now becoming a global concern and has even been set as one the SDGs, specifically as SDG target 5.3.\n\n### Limitations and exceptions:\nThe data on FGM should be interpreted with caution for several reasons. Women may be reluctant to disclose undergoing FGM due to its sensitivity or illegal status, and some may be unaware of the procedure, especially if performed at an early age. The data is retrospective, not reflecting recent changes, with reports from girls aged 15 to 19 years referring to events 14 to 18 years earlier. Surveys like MICS and DHS only include FGM questions in countries where the practice is prevalent, meaning FGM may still exist in countries without data, including high-income countries with migrant populations and certain low- and middle-income countries. National-level estimates may be misleading as FGM is often practiced by specific ethnic groups in certain locations, thus not accurately representing the prevalence. Reference: A Generation to Protect: Monitoring violence exploitation and abuse of children within the SDG framework (UNICEF 2020). https://data.unicef.org/wp-content/uploads/2020/06/A-Generation-to-Protect-publication-English_2020.pdf\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 5.3.2[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UNICEF, Demographic and Health Surveys (DHS), and Multiple Indicator Cluster Surveys (MICS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF DATA, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), type: API, date accessed: 2023-12-07;\nDemographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and other surverys, Demographic and Health Surveys (DHS) Program, uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), publisher: The DHS Program (ICF), type: API, date accessed: 2023-12-07;\nDHS API, Demographic and Health Surveys (DHS) Program, uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: PT_F_15-49_FGM; \tIndicator name from the original source: Percentage of girls and women (aged 15-49 years) who have undergone female genital mutilation (FGM), date accessed: 2023-12-07. Indicator SH.STA.FGMS.ZS (https://data.worldbank.org/indicator/SH.STA.FGMS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.FGMS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sh_sta_hygn_ru_zs": { "title": "People with basic handwashing facilities including soap and water, rural (% of rural population)", "description_from_producer": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys.\n\n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data.\n\n### Development relevance:\nHygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior.\n\n### Limitations and exceptions:\nGenerally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data.\n\n### Other notes:\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.HYGN.RU.ZS (https://data.worldbank.org/indicator/SH.STA.HYGN.RU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.HYGN.RU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of rural population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of rural population" } }, "sh_sta_hygn_ur_zs": { "title": "People with basic handwashing facilities including soap and water, urban (% of urban population)", "description_from_producer": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys.\n\n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data.\n\n### Development relevance:\nHygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior.\n\n### Limitations and exceptions:\nGenerally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data.\n\n### Other notes:\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.HYGN.UR.ZS (https://data.worldbank.org/indicator/SH.STA.HYGN.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.HYGN.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of urban population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of urban population" } }, "sh_sta_hygn_zs": { "title": "People with basic handwashing facilities including soap and water (% of population)", "description_from_producer": "The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Utilizing national-level data derived from household surveys, mainly the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), the JMP calculates the proportion of the population with access to basic handwashing facilities at home with soap and water for each country by using a simple linear regression.\nStatistical concept(s): This indicator is measured using a straightforward indicator that examines the presence of handwashing facilities with soap within homes mainly through national household surveys.\n\n\n\n\n\n\n\n\n\n\n\nCollecting accurate information on handwashing practices presents challenges. Self-reported handwashing is an unreliable measure due to the potential for inaccurate reporting. Direct observation of handwashing can lead to observer bias, as individuals may alter their behavior when they know they are being watched, and implementing such observations on a large scale is resource-intensive. A more effective method involves survey enumerators observing the designated handwashing areas in homes and verifying the availability of water and soap, or a local substitute. This approach provides a more dependable and practical measure of handwashing behavior than relying on self-reported data.\n\n### Development relevance:\nHygiene is closely correlated with human health. Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations. Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises, and is a useful proxy for hygienic behavior.\n\n### Limitations and exceptions:\nGenerally, data on handwashing facilities are limited in high-income countries due to the infrequent collection of such information. In the early 2000s, even low- and middle-income countries often lacked this data. However, the recent standardization of hygiene-related questions in international surveys has led to an improvement in the availability of data.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.HYGN.ZS (https://data.worldbank.org/indicator/SH.STA.HYGN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.HYGN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "sh_sta_maln_fe_zs": { "title": "Prevalence of underweight, weight for age, female (% of children under 5)", "description_from_producer": "Prevalence of underweight, female, is the percentage of girls under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards.\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.\n\n### Other notes:\nUndernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.MALN.FE.ZS (https://data.worldbank.org/indicator/SH.STA.MALN.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.MALN.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_sta_maln_ma_zs": { "title": "Prevalence of underweight, weight for age, male (% of children under 5)", "description_from_producer": "Prevalence of underweight, male, is the percentage of boys under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards.\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.\n\n### Other notes:\nUndernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.MALN.MA.ZS (https://data.worldbank.org/indicator/SH.STA.MALN.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.MALN.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_sta_maln_zs": { "title": "Prevalence of underweight, weight for age (% of children under 5)", "description_from_producer": "Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 2006 Child Growth Standards.\n\n### Aggregation method:\nLinear mixed-effect model estimates\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child underweight belongs to a set of indicators whose purpose is to measure nutritional imbalance and malnutrition resulting in undernutrition (assessed by underweight, stunting and wasting) and overweight.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.\n\n### Other notes:\nUndernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.MALN.ZS (https://data.worldbank.org/indicator/SH.STA.MALN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.MALN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_sta_mmrt": { "title": "Maternal mortality ratio (modeled estimate, per 100,000 live births)", "description_from_producer": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMaternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.\n\n### Development relevance:\nChildbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants.\n\n### Limitations and exceptions:\nThe methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality.\n\n### Other notes:\nThis indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator (3.1.1) for monitoring maternal health.", "origins": [ { "producer": "World Health Organization, UNICEF, UNFPA, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Trends in Maternal Mortality, World Health Organization (WHO), uri: https://www.who.int/news/item/23-02-2023-a-woman-dies-every-two-minutes-due-to-pregnancy-or-childbirth--un-agencies;\nUN Children's Fund (UNICEF), note: Trends in Maternal Mortality;\nUN Population Fund (UNFPA), note: Trends in Maternal Mortality;\nWorld Bank Group (WBG), note: Trends in Maternal Mortality. Indicator SH.STA.MMRT (https://data.worldbank.org/indicator/SH.STA.MMRT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.MMRT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled estimate, per 100,000 live births", "short_unit": "modeled estimate,", "display": { "shortUnit": "modeled estimate,", "unit": "modeled estimate, per 100,000 live births" } }, "sh_sta_mmrt_ne": { "title": "Maternal mortality ratio (national estimate, per 100,000 live births)", "description_from_producer": "Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.\n\n### Statistical concept and methodology:\nMethodology: The national estimates of maternal mortality ratios are based on national surveys, vital registration records, and surveillance data or are derived from community and hospital records.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.\n\n### Development relevance:\nChildbirth should be a time of life, not death. And yet, many women die due to complications of pregnancy and childbirth. Nearly every death is in low- and middle-income countries, and nearly every death is preventable where the clinical knowledge and technology required to prevent them have existed. However these solutions are often not available, not accessible or not implemented, especially in low-resource settings and/or subpopulations at greater risk due to social determinants.\n\n### Limitations and exceptions:\nMaternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality.\n\nMaternal mortality ratios collected directly from Demographic and Health Surveys are presented in the survey year, but reference time of these maternal mortality ratios is for the seven years preceding the survey.", "origins": [ { "producer": "UN Maternal Mortality Estimation Inter-Agency Group (MMEIG), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Maternal Mortality Estimation Inter-Agency Group (MMEIG), World Health Organization (WHO), note: The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources: civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.;\nUN Children's Fund (UNICEF), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nUN Population Fund (UNFPA), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nWorld Bank Group (WBG), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nUnited Nations (UN), note: Maternal Mortality Estimation Inter-Agency Group (MMEIG);\nPan American Health Organization (PAHO), note: Core Indicators Portal;\nDemographic and Health Surveys (DHS) Program, note: The DHS Program, Demographic and Health Surveys. Indicator SH.STA.MMRT.NE (https://data.worldbank.org/indicator/SH.STA.MMRT.NE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.MMRT.NE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate, per 100,000 live births", "short_unit": "national estimate,", "display": { "shortUnit": "national estimate,", "unit": "national estimate, per 100,000 live births" } }, "sh_sta_odfc_ru_zs": { "title": "People practicing open defecation, rural (% of rural population)", "description_from_producer": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation).\n\n### Development relevance:\nAccess to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue.\n\n### Limitations and exceptions:\nNational, regional and income group estimates are made when data are available for at least 50 percent of the population.\n\n### Other notes:\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.ODFC.RU.ZS (https://data.worldbank.org/indicator/SH.STA.ODFC.RU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.ODFC.RU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of rural population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of rural population" } }, "sh_sta_odfc_ur_zs": { "title": "People practicing open defecation, urban (% of urban population)", "description_from_producer": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation).\n\n### Development relevance:\nAccess to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue.\n\n### Limitations and exceptions:\nNational, regional and income group estimates are made when data are available for at least 50 percent of the population.\n\n### Other notes:\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.ODFC.UR.ZS (https://data.worldbank.org/indicator/SH.STA.ODFC.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.ODFC.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of urban population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of urban population" } }, "sh_sta_odfc_zs": { "title": "People practicing open defecation (% of population)", "description_from_producer": "People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation).\n\n### Development relevance:\nAccess to sanitation is a fundamental human right. Open defecation, which is the practice of relieving oneself outside without proper facilities, is a violation of human dignity and poses a significant threat to public health and nutrition. Poor sanitation is a leading cause of infectious diseases globally, and enhancing sanitation services has been proven to have a substantial positive effect on health outcomes. The provision of basic and safely managed sanitation can decrease the incidence of diarrheal diseases and mitigate the health consequences of other serious illnesses that cause widespread morbidity and mortality among children. Diarrheal conditions and parasitic infections debilitate children, increasing their vulnerability to malnutrition and secondary infections such as pneumonia, measles, and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe absence of adequate sanitation is especially harmful to women who are forced to defecate in the open, as it compromises their privacy and exposes them to a greater risk of assault and violence. The elimination of open defecation is a specific target within the Sustainable Development Goals (SDG target 6.2), underscoring the international commitment to addressing this critical issue.\n\n### Limitations and exceptions:\nNational, regional and income group estimates are made when data are available for at least 50 percent of the population.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.ODFC.ZS (https://data.worldbank.org/indicator/SH.STA.ODFC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.ODFC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "sh_sta_orcf_zs": { "title": "Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)", "description_from_producer": "Children with diarrhea who received oral rehydration and continued feeding refer to the percentage of children under age five with diarrhea in the two weeks prior to the survey who received either oral rehydration therapy or increased fluids, with continued feeding.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Mothers or caregivers of children under five years old are asked whether the child experienced diarrhea at any point in the past two weeks. If the child did have diarrhea, they are further asked whether either oral rehydration therapy or increased fluids, with continued feeding was administered. The term \"diarrhea,\" as defined by the DHS, should include all forms of diarrhea, such as bloody stools (indicative of dysentery), watery stools, and other variations. This definition encompasses both the mother's understanding and locally-used terms.\n\n### Development relevance:\nMost diarrhea-related deaths are due to dehydration, and many of these deaths can be prevented with the use of oral rehydration salts at home.\n\n### Limitations and exceptions:\nRecommendations for the use of oral rehydration therapy have changed over time based on scientific progress, so it is difficult to accurately compare use rates across countries. Until the current recommended method for home management of diarrhea is adopted and applied in all countries, the data should be used with caution. Also, the prevalence of diarrhea may vary by season. Since country surveys are administered at different times, data comparability is further affected.", "origins": [ { "producer": "Demographic and Health Surveys (DHS) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys (DHS) Program, Demographic and Health Surveys (DHS) Program. Indicator SH.STA.ORCF.ZS (https://data.worldbank.org/indicator/SH.STA.ORCF.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.ORCF.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5 receiving oral rehydration and continued feeding", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5 receiving oral rehydration and continued feeding" } }, "sh_sta_orth": { "title": "Diarrhea treatment (% of children under 5 who received ORS packet)", "description_from_producer": "Percentage of children under age 5 with diarrhea in the two weeks preceding the survey who received oral rehydration salts (ORS packets or pre-packaged ORS fluids).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Mothers or caregivers of children under five years old are asked whether the child experienced diarrhea at any point in the past two weeks. If the child did have diarrhea, they are further asked whether Oral Rehydration Solution (ORS) was administered. The term \"diarrhea,\" as defined by the DHS, should include all forms of diarrhea, such as bloody stools (indicative of dysentery), watery stools, and other variations. This definition encompasses both the mother's understanding and locally-used terms.\n\n### Limitations and exceptions:\nThe indicator does not assess the severity of the diarrhea.", "origins": [ { "producer": "Demographic and Health Surveys (DHS) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "State of the World's Children, UN Children's Fund (UNICEF);\nChildinfo, UN Children's Fund (UNICEF);\nDemographic and Health Surveys, Demographic and Health Surveys (DHS) Program. Indicator SH.STA.ORTH (https://data.worldbank.org/indicator/SH.STA.ORTH). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.ORTH", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5 who received ORS packet", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5 who received ORS packet" } }, "sh_sta_owgh_fe_zs": { "title": "Prevalence of overweight, weight for height, female (% of children under 5)", "description_from_producer": "Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.\n\n### Other notes:\nEstimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Bl\u00f6ssner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.OWGH.FE.ZS (https://data.worldbank.org/indicator/SH.STA.OWGH.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.OWGH.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_sta_owgh_ma_zs": { "title": "Prevalence of overweight, weight for height, male (% of children under 5)", "description_from_producer": "Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.\n\n### Other notes:\nEstimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Bl\u00f6ssner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.OWGH.MA.ZS (https://data.worldbank.org/indicator/SH.STA.OWGH.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.OWGH.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_sta_owgh_me_fe_zs": { "title": "Prevalence of overweight, weight for height, female (modeled estimate, % of children under 5)", "description_from_producer": "Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.\n\n### Periodicity:\nEvery two years\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time.\n\n### Other notes:\nOnce considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Bl\u00f6ssner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME). Indicator SH.STA.OWGH.ME.FE.ZS (https://data.worldbank.org/indicator/SH.STA.OWGH.ME.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.OWGH.ME.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_sta_owgh_me_ma_zs": { "title": "Prevalence of overweight, weight for height, male (modeled estimate, % of children under 5)", "description_from_producer": "Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.\n\n### Periodicity:\nEvery two years\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time.\n\n### Other notes:\nOnce considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Bl\u00f6ssner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME). Indicator SH.STA.OWGH.ME.MA.ZS (https://data.worldbank.org/indicator/SH.STA.OWGH.ME.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.OWGH.ME.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_sta_owgh_me_zs": { "title": "Prevalence of overweight, weight for height (modeled estimate, % of children under 5)", "description_from_producer": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.\n\n### Periodicity:\nEvery two years\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time.\n\n### Other notes:\nOnce considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Bl\u00f6ssner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME). Indicator SH.STA.OWGH.ME.ZS (https://data.worldbank.org/indicator/SH.STA.OWGH.ME.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.OWGH.ME.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_sta_owgh_zs": { "title": "Prevalence of overweight, weight for height (% of children under 5)", "description_from_producer": "Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.\n\n### Aggregation method:\nSee SH.STA.OWGH.ME.ZS for aggregation\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child overweight refers to a child who is too heavy for his or her height. This form of malnutrition results from expending too few calories for the amount of food consumed and increases the risk of noncommunicable diseases later in life.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.\n\n### Other notes:\nEstimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Bl\u00f6ssner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.OWGH.ZS (https://data.worldbank.org/indicator/SH.STA.OWGH.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.OWGH.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_sta_pois_p5": { "title": "Mortality rate attributed to unintentional poisoning (per 100,000 population)", "description_from_producer": "Mortality rate attributed to unintentional poisonings is the number of deaths from unintentional poisonings in a year per 100,000 population. Unintentional poisoning can\n\n\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf\n\n### Development relevance:\nMortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country\u2019s health system.\n\n### Limitations and exceptions:\nSome countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.9.3[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO), Global Health Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.STA.POIS.P5 (https://data.worldbank.org/indicator/SH.STA.POIS.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.POIS.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 population", "display": { "unit": "per 100,000 population" } }, "sh_sta_pois_p5_fe": { "title": "Mortality rate attributed to unintentional poisoning, female (per 100,000 female population)", "description_from_producer": "Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf\n\n### Development relevance:\nMortality rates due to unintentional poisoning remains relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country\u2019s health system.\n\n### Limitations and exceptions:\nSome countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries.\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO), Global Health Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.STA.POIS.P5.FE (https://data.worldbank.org/indicator/SH.STA.POIS.P5.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.POIS.P5.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 female population", "display": { "unit": "per 100,000 female population" } }, "sh_sta_pois_p5_ma": { "title": "Mortality rate attributed to unintentional poisoning, male (per 100,000 male population)", "description_from_producer": "Mortality rate attributed to unintentional poisonings is the number of male deaths from unintentional poisonings in a year per 100,000 male population. Unintentional poisoning can\n\n\nbe caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The estimates for unintentional poisoning mortality are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database are used, with necessary adjustments for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. For countries lacking high-quality death registration data, cause of death estimates are calculated using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not align with official national estimates. The complete methodology can be found at the following: https://www.who.int/docs/defaultsource/gho-documents/global-health-estimates/ghe2019_cod_methods.pdf\n\n### Development relevance:\nMortality rates due to unintentional poisoning remain relatively high in low income countries. This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country's health system.\n\n### Limitations and exceptions:\nSome countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other types of information for these countries.\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO), Global Health Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.STA.POIS.P5.MA (https://data.worldbank.org/indicator/SH.STA.POIS.P5.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.POIS.P5.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 male population", "display": { "unit": "per 100,000 male population" } }, "sh_sta_smss_ru_zs": { "title": "People using safely managed sanitation services, rural (% of rural population)", "description_from_producer": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, composting toilets or pit latrines with slabs.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, composting toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation).\n\n### Development relevance:\nSanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired.\n\n### Limitations and exceptions:\nThere are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporarily and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population.\n\n### Other notes:\nAggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme (JMP) for WASH and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.SMSS.RU.ZS (https://data.worldbank.org/indicator/SH.STA.SMSS.RU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.SMSS.RU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of rural population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of rural population" } }, "sh_sta_smss_ur_zs": { "title": "People using safely managed sanitation services, urban (% of urban population)", "description_from_producer": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, composting toilets or pit latrines with slabs.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, composting toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation).\n\n### Development relevance:\nSanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired.\n\n### Limitations and exceptions:\nThere are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporarily and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population.\n\n### Other notes:\nAggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\n\n\nThis is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme (JMP) for WASH and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.SMSS.UR.ZS (https://data.worldbank.org/indicator/SH.STA.SMSS.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.SMSS.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_sta_smss_zs": { "title": "People using safely managed sanitation services (% of population)", "description_from_producer": "The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, composting toilets or pit latrines with slabs.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data sources for sanitation services are household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS), administrative data, census, and other datasets such as compilations by international or regional initiatives (e.g., IB-NET) or studies conducted by research institutions. Based on these national datasets, JMP estimates the proportion of the people accessing different levels of services by using linear regression. You can find the details of estimates including the rules on interpolation, extrapolation and extension in the JMP\u2019s methodology report (https://washdata.org/reports/jmp-2017-methodology).\nStatistical concept(s): Safely managed sanitation facilities are defined as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, composting toilets or pit latrines with slabs. The JMP classifies the sanitation service levels into five tiers, ranging from the most to the least favorable: safely managed, basic, limited, unimproved, and open defecation (Reference: https://washdata.org/monitoring/sanitation).\n\n### Development relevance:\nSanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThe combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMost basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nChildren bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired.\n\n### Limitations and exceptions:\nThere are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporarily and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site). Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system. If no information is available, it is assumed that 50 percent is safely managed. Regional and income group estimates are made when data are available for at least 30 percent of the population.\n\n### Other notes:\nAggregate data by groups are computed based on the World Bank fiscal year groupings in effect at the time the data were released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.\n\n\n\n\n\nThis indicator (Total) is calculated as a population-weighted average of the RURAL and URBAN aggregates when sufficient data are available for both domains. When coverage at either the RURAL or URBAN level is insufficient, but country-level TOTAL data meet the minimum population coverage threshold (30%), TOTAL aggregates are calculated directly from country-level TOTAL estimates. Because these two aggregation approaches may be applied in different years as data availability improves, methodological switches can occur and may result in discontinuities in the time series.\n\n\n\n\n\nThis indicator corresponds to Sustainable Development Goal indicator 6.2.1 (see UN SDG metadata: https://unstats.un.org/sdgs/metadata/).", "origins": [ { "producer": "WHO/UNICEF Joint Monitoring Programme (JMP) for WASH and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene, World Health Organization (WHO), uri: washdata.org, note: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene., publisher: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org), date accessed: 2025-09-30, date published: 2025-08-25;\nUN Children's Fund (UNICEF), uri: washdata.org, note: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. Indicator SH.STA.SMSS.ZS (https://data.worldbank.org/indicator/SH.STA.SMSS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.SMSS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "sh_sta_stnt_fe_zs": { "title": "Prevalence of stunting, height for age, female (% of children under 5)", "description_from_producer": "Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.\n\n### Other notes:\nUndernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.STNT.FE.ZS (https://data.worldbank.org/indicator/SH.STA.STNT.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.STNT.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_sta_stnt_ma_zs": { "title": "Prevalence of stunting, height for age, male (% of children under 5)", "description_from_producer": "Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.\n\n### Other notes:\nUndernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.STNT.MA.ZS (https://data.worldbank.org/indicator/SH.STA.STNT.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.STNT.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_sta_stnt_me_fe_zs": { "title": "Prevalence of stunting, height for age, female (modeled estimate, % of children under 5)", "description_from_producer": "Prevalence of stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time.\n\n### Other notes:\nOnce considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Bl\u00f6ssner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME). Indicator SH.STA.STNT.ME.FE.ZS (https://data.worldbank.org/indicator/SH.STA.STNT.ME.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.STNT.ME.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_sta_stnt_me_ma_zs": { "title": "Prevalence of stunting, height for age, male (modeled estimate, % of children under 5)", "description_from_producer": "Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.\n\n### Periodicity:\nEvery two years\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time.\n\n### Other notes:\nOnce considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Bl\u00f6ssner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.\n\n\n\nEstimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME);\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME). Indicator SH.STA.STNT.ME.MA.ZS (https://data.worldbank.org/indicator/SH.STA.STNT.ME.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.STNT.ME.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_sta_stnt_me_zs": { "title": "Share of children who are stunted (modeled estimates)", "description_short": "The share of children younger than five years old whose growth is stunted.", "description_from_producer": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006.\n\n[Text from World Bank World Development Indicators [metadata glossary](https://databank.worldbank.org/metadataglossary/world-development-indicators/series/SH.STA.STNT.ME.ZS)]", "description_key": "- Stunting is when a child is significantly shorter than the average for their age.\n- Stunted growth is a consequence of poor nutrition and/or repeated infection.\n- These values are model estimates which account for sampling error around survey estimates and the data producer has made efforts to ensure the data is comparable over countries and time.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Joint child Malnutrition Estimates (JME), UN Children's Fund (UNICEF), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, publisher: JME;\nWorld Health Organization (WHO), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME), publisher: JME;\nWorld Bank (WB), uri: https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb, note: Joint child Malnutrition Estimates (JME), publisher: JME. Indicator SH.STA.STNT.ME.ZS (https://data.worldbank.org/indicator/SH.STA.STNT.ME.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.STNT.ME.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" }, "presentation": { "grapher_config": { "title": "Malnutrition: Share of children who are stunted", "subtitle": "The share of children younger than five years old that are defined as stunted. [Stunting](#dod:stunting) is when a child is significantly shorter than the average for their age. It is a consequence of poor nutrition and/or repeated infection.", "sourceDesc": "UNICEF, World Health Organization and World Bank; modeled estimates", "hideAnnotationFieldsInTitle": { "time": true, "entity": true, "changeInPrefix": true }, "hasMapTab": true, "tab": "map", "variantName": "UNICEF, World Health Organization and World Bank", "originUrl": "https://ourworldindata.org/hunger-and-undernourishment", "yAxis": { "min": 0 }, "map": { "timeTolerance": 5, "colorScale": { "baseColorScheme": "YlOrRd", "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 80 ], "customNumericLabels": [ "", "", "", "", "", "", "", "", "", "" ], "customNumericColors": [ null, null ], "legendDescription": "Prevalence of stunting (% of children under 5)" } }, "selectedEntityNames": [ "China", "Colombia", "Kenya", "Peru", "Bangladesh", "United States" ], "relatedQuestions": [ { "url": "https://ourworldindata.org/grapher/share-of-children-younger-than-5-who-suffer-from-stunting#faqs", "text": "FAQs on this data" } ] }, "title_public": "Share of children who are stunted", "attribution_short": "UNICEF/WHO (via World Bank)", "attribution": "UNICEF; World Health Organization; World Bank", "topic_tags": [ "Hunger & Undernourishment", "Micronutrient Deficiency" ], "faqs": [ { "gdoc_id": "1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw", "fragment_id": "stunting-definition" }, { "gdoc_id": "1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw", "fragment_id": "stunting-measurement" }, { "gdoc_id": "1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw", "fragment_id": "stunting-causes" } ] } }, "sh_sta_stnt_zs": { "title": "Share of children who are stunted", "description_short": "The share of children younger than five years old whose growth is stunted.", "description_from_producer": "Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006.\n\n[Text from World Bank World Development Indicators [metadata glossary](https://databank.worldbank.org/metadataglossary/world-development-indicators/series/SH.STA.STNT.ZS)]", "description_key": "- Stunting is when a child is significantly shorter than the average for their age.\n- Stunted growth is a consequence of poor nutrition and/or repeated infection.\n- These values are survey estimates which come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.STNT.ZS (https://data.worldbank.org/indicator/SH.STA.STNT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.STNT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" }, "presentation": { "grapher_config": { "title": "Malnutrition: Share of children who are stunted", "subtitle": "The share of children younger than five years old that are defined as stunted. [Stunting](#dod:stunting) is when a child is significantly shorter than the average for their age. It is a consequence of poor nutrition and/or repeated infection.", "sourceDesc": "UNICEF, World Health Organization and World Bank", "hideAnnotationFieldsInTitle": { "time": true, "entity": true, "changeInPrefix": true }, "hasMapTab": true, "tab": "map", "variantName": "UNICEF, World Health Organization and World Bank", "originUrl": "https://ourworldindata.org/hunger-and-undernourishment", "yAxis": { "min": 0 }, "map": { "timeTolerance": 5, "colorScale": { "baseColorScheme": "YlOrRd", "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 80 ], "customNumericLabels": [ "", "", "", "", "", "", "", "", "", "" ], "customNumericColors": [ null, null ], "legendDescription": "Prevalence of stunting (% of children under 5)" } }, "selectedEntityNames": [ "China", "Colombia", "Kenya", "Peru", "Bangladesh", "United States" ], "relatedQuestions": [ { "url": "https://ourworldindata.org/grapher/share-of-children-younger-than-5-who-suffer-from-stunting#faqs", "text": "FAQs on this data" } ] }, "title_public": "Share of children who are stunted", "attribution_short": "UNICEF/WHO (via World Bank)", "attribution": "UNICEF; World Health Organization; World Bank", "topic_tags": [ "Hunger & Undernourishment", "Micronutrient Deficiency" ], "faqs": [ { "gdoc_id": "1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw", "fragment_id": "stunting-definition" }, { "gdoc_id": "1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw", "fragment_id": "stunting-measurement" }, { "gdoc_id": "1gGburArxglFdHXeTLotFW4TOOLoeRq5XW6UfAdKtaAw", "fragment_id": "stunting-causes" } ] } }, "sh_sta_suic_fe_p5": { "title": "Suicide mortality rate, female (per 100,000 female population)", "description_from_producer": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates.\n\n### Limitations and exceptions:\nDue to changes in input data and methods, the revisions of the data are not comparable to previously published estimates.\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO), Global Health Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.STA.SUIC.FE.P5 (https://data.worldbank.org/indicator/SH.STA.SUIC.FE.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.SUIC.FE.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_sta_suic_ma_p5": { "title": "Suicide mortality rate, male (per 100,000 male population)", "description_from_producer": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates.\n\n### Limitations and exceptions:\nDue to changes in input data and methods, the revisions of the data are not comparable to previously published estimates.\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO), Global Health Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.STA.SUIC.MA.P5 (https://data.worldbank.org/indicator/SH.STA.SUIC.MA.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.SUIC.MA.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_sta_suic_p5": { "title": "Suicide mortality rate (per 100,000 population)", "description_from_producer": "Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: It is calculated by dividing the number of suicide deaths in a year by the mid-year population for the same calendar year, then multiplying by 100,000. The estimates are derived from the WHO Global Health Estimates (GHE). For countries with high-quality vital registration systems that include information on cause of death, the vital registration data submitted by member states to the WHO Mortality Database are used, with adjustments made where necessary (e.g., for under-reporting of deaths, unknown age and sex, and ill-defined causes of death). For countries without high-quality death registration data, cause of death estimates are calculated using other sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies. These estimates represent WHO's best calculations, using standard categories, definitions, and methods to ensure cross-country comparability. They may not be identical to official national estimates.\n\n### Limitations and exceptions:\nDue to changes in input data and methods, the revisions of the data are not comparable to previously published estimates.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO), Global Health Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.STA.SUIC.P5 (https://data.worldbank.org/indicator/SH.STA.SUIC.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.SUIC.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 population", "display": { "unit": "per 100,000 population" } }, "sh_sta_traf_p5": { "title": "Mortality caused by road traffic injury (per 100,000 population)", "description_from_producer": "Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The methods used for analyzing causes of death vary based on the type of data available from different countries. For countries with high-quality vital registration systems that include information on cause of death, the data submitted by member states to the WHO Mortality Database is utilized, with necessary adjustments made for factors such as under-reporting of deaths, unknown age and sex, and ill-defined causes of death. In contrast, for countries lacking high-quality death registration data, cause of death estimates are derived using alternative sources, including household surveys with verbal autopsy, sample or sentinel registration systems, and special studies.\n\n### Development relevance:\nRoad traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world. There is a strong association between the risk of road traffic death and the income level of countries. The burden of road traffic deaths is disproportionately high among low- and middle-income countries.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.6.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO), Global Health Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.STA.TRAF.P5 (https://data.worldbank.org/indicator/SH.STA.TRAF.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.TRAF.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 people", "display": { "unit": "per 100,000 people" } }, "sh_sta_wash_p5": { "title": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)", "description_from_producer": "Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: To estimate the portion of deaths from diarrhea and acute respiratory infections attributable to unsafe water, sanitation, and hygiene (WASH), a comparative risk assessment approach is used. This involves calculating the attributable disease deaths by combining information on the increased (or relative) risk of a disease resulting from exposure with the prevalence of that exposure in the population. This calculation yields the 'population attributable fraction' (PAF), which represents the fraction of disease in a population that can be attributed to the exposure, in this case, unsafe WASH.\n\n\nBy applying the PAF to the total deaths from diarrhea or acute respiratory infections, the number of deaths resulting from inadequate WASH can be determined. Additionally, deaths from protein-energy malnutrition attributable to inadequate WASH are estimated by evaluating the impacts of repeated infectious diarrhea episodes on nutritional status, particularly stunting. All deaths from intestinal nematode infections are attributed to inadequate WASH due to their transmission pathway.\n\n### Development relevance:\nUnsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death. Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene. According to the World Health Organization, in addition to diarrhea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria.\n\n### Limitations and exceptions:\nSome countries do not have death registration data or sample registration systems. The estimates on this indicator need to be completed with other type of information for these countries.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.9.2[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "World Health Organization (WHO), Global Health Observatory, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/)., World Health Organization (WHO), uri: https://www.who.int/data/gho. Indicator SH.STA.WASH.P5 (https://data.worldbank.org/indicator/SH.STA.WASH.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.WASH.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 population", "display": { "unit": "per 100,000 population" } }, "sh_sta_wast_fe_zs": { "title": "Prevalence of wasting, weight for height, female (% of children under 5)", "description_from_producer": "Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months.\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided.\n\n### Other notes:\nUndernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.WAST.FE.ZS (https://data.worldbank.org/indicator/SH.STA.WAST.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.WAST.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_sta_wast_ma_zs": { "title": "Prevalence of wasting, weight for height, male (% of children under 5)", "description_from_producer": "Prevalence of wasting, male, is the proportion of boys under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months.\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided.\n\n### Other notes:\nUndernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.WAST.MA.ZS (https://data.worldbank.org/indicator/SH.STA.WAST.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.WAST.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_sta_wast_zs": { "title": "Prevalence of wasting, weight for height (% of children under 5)", "description_from_producer": "Prevalence of wasting is the proportion of children under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59 months.\n\n### Aggregation method:\nLinear mixed-effect model estimates\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided.\n\n### Other notes:\nUndernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.STA.WAST.ZS (https://data.worldbank.org/indicator/SH.STA.WAST.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.STA.WAST.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_svr_wast_fe_zs": { "title": "Prevalence of severe wasting, weight for height, female (% of children under 5)", "description_from_producer": "Prevalence of severe wasting, female, is the proportion of girls under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months.\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided.\n\n### Other notes:\nUndernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.SVR.WAST.FE.ZS (https://data.worldbank.org/indicator/SH.SVR.WAST.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.SVR.WAST.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_svr_wast_ma_zs": { "title": "Prevalence of severe wasting, weight for height, male (% of children under 5)", "description_from_producer": "Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months.\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided.\n\n### Other notes:\nUndernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.SVR.WAST.MA.ZS (https://data.worldbank.org/indicator/SH.SVR.WAST.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.SVR.WAST.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_svr_wast_zs": { "title": "Prevalence of severe wasting, weight for height (% of children under 5)", "description_from_producer": "Prevalence of severe wasting is the proportion of children under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months.\n\n### Aggregation method:\nLinear mixed-effect model estimates\n\n### Statistical concept and methodology:\nMethodology: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described elsewhere.\nStatistical concept(s): Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible.\n\n### Development relevance:\nChild growth is an internationally accepted outcome reflecting child nutritional status.\n\n### Limitations and exceptions:\nSurvey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided.\n\n### Other notes:\nUndernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF). Estimates are from national survey data. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.", "origins": [ { "producer": "Joint child Malnutrition Estimates (JME) - UNICEF, WHO, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology., UN Children's Fund (UNICEF), note: Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Health Organization (WHO), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology;\nWorld Bank (WB), note: Joint child Malnutrition Estimates (JME); Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology. Indicator SH.SVR.WAST.ZS (https://data.worldbank.org/indicator/SH.SVR.WAST.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.SVR.WAST.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children under 5", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children under 5" } }, "sh_tbs_cure_zs": { "title": "Tuberculosis treatment success rate (% of new cases)", "description_from_producer": "Tuberculosis treatment success rate is the percentage of all new tuberculosis cases (or new and relapse cases for some countries) registered under a national tuberculosis control programme in a given year that successfully completed treatment, with or without bacteriological evidence of success (\"cured\" and \"treatment completed\" respectively).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of cases registered in a given year (excluding cases placed on a second-line drug regimen) that successfully completed treatment without bacteriological evidence of failure. All registered cases fall into one of the following five mutually exclusive categories: treatment success, failure, death, lost to follow-up, not evaluated (missing data on the outcome of treatment).\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. Data on the success rate of tuberculosis treatment are provided for countries that have submitted data to the WHO. The treatment success rate for tuberculosis provides a useful indicator of the quality of health services. A low rate suggests that infectious patients may not be receiving adequate treatment. An important complement to the tuberculosis treatment success rate is the case detection rate, which indicates whether there is adequate coverage by the recommended case detection and treatment strategy.\n\n### Development relevance:\nTuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world\u2019s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year.\n\n### Other notes:\nAggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization.", "origins": [ { "producer": "Global Tuberculosis Report - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Tuberculosis Report, World Health Organization (WHO). Indicator SH.TBS.CURE.ZS (https://data.worldbank.org/indicator/SH.TBS.CURE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.TBS.CURE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of new cases", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of new cases" } }, "sh_tbs_dtec_zs": { "title": "Tuberculosis case detection rate (%, all forms)", "description_from_producer": "Tuberculosis case detection rate (all forms) is the number of new and relapse tuberculosis cases notified to WHO in a given year, divided by WHO's estimate of the number of incident tuberculosis cases for the same year, expressed as a percentage. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The number of new and relapse TB cases diagnosed and treated in national TB control programmes and notified to WHO, divided by WHO's estimate of the number of incident TB cases for the same year, expressed as a percentage.\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. This indicator shows the tuberculosis detection rate for all detection methods. Editions before 2010 included the tuberculosis detection rates by DOTS, the internationally recommended strategy for tuberculosis control. Thus data on the case detection rate from 2010 onward cannot be compared with data in previous editions.\n\n### Development relevance:\nTuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world\u2019s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year.\n\n### Other notes:\nAggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization.", "origins": [ { "producer": "Global Tuberculosis Report - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Tuberculosis Report, World Health Organization (WHO). Indicator SH.TBS.DTEC.ZS (https://data.worldbank.org/indicator/SH.TBS.DTEC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.TBS.DTEC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%, all forms", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%, all forms" } }, "sh_tbs_incd": { "title": "Incidence of tuberculosis (per 100,000 people)", "description_from_producer": "Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are based on annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease and on information from death (vital) registration systems.\nStatistical concept(s): Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. In developed countries tuberculosis has reemerged largely as a result of cases among immigrants. Since tuberculosis incidence cannot be directly measured, estimates are obtained by eliciting expert opinion or are derived from measurements of prevalence or mortality.\n\n### Development relevance:\nTuberculosis (TB) is a preventable and usually curable disease. Yet TB is one of the world\u2019s leading causes of death from a single infectious agent. Millions of people continue to fall ill with TB every year.\n\n### Limitations and exceptions:\nThe limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.\n\n\n\nUncertainty bounds for the incidence are available at http://data.worldbank.org\n\n### Other notes:\nAggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization.\n\nThis is the Sustainable Development Goal indicator 3.3.2[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Global Tuberculosis Report - World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Tuberculosis Report, World Health Organization (WHO). Indicator SH.TBS.INCD (https://data.worldbank.org/indicator/SH.TBS.INCD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.TBS.INCD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 people", "display": { "unit": "per 100,000 people" } }, "sh_vac_ttns_zs": { "title": "Newborns protected against tetanus (%)", "description_from_producer": "Newborns protected against tetanus are the percentage of births by women of child-bearing age who are immunized against tetanus.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data shown here are based on an assessment of national immunization coverage rates by the WHO and UNICEF. The assessment considered both administrative data from service providers and household survey data on children's immunization histories. Based on the data available, consideration of potential biases, and contributions of local experts, the most likely true level of immunization coverage was determined for each year.\nStatistical concept(s): Governments in developing countries usually finance immunization against measles and diphtheria, pertussis (whooping cough), and tetanus (DTP) as part of the basic public health package.\n\n### Development relevance:\nImmunization is one of the most cost-effective public health interventions, and is an essential component for reducing under-five mortality. Immunization coverage estimates are used to monitor coverage of immunization services and to guide disease eradication and elimination efforts.", "origins": [ { "producer": "World Health Organization (WHO) and UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Health Organization (WHO), uri: http://www.who.int/immunization/monitoring_surveillance/en/;\nUN Children's Fund (UNICEF). Indicator SH.VAC.TTNS.ZS (https://data.worldbank.org/indicator/SH.VAC.TTNS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.VAC.TTNS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sh_xpd_chex_gd_zs": { "title": "Current health expenditure (% of GDP)", "description_from_producer": "Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: https://apps.who.int/nha/database. Indicator SH.XPD.CHEX.GD.ZS (https://data.worldbank.org/indicator/SH.XPD.CHEX.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.CHEX.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "sh_xpd_chex_pc_cd": { "title": "Current health expenditure per capita (current US$)", "description_from_producer": "Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.CHEX.PC.CD (https://data.worldbank.org/indicator/SH.XPD.CHEX.PC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.CHEX.PC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "sh_xpd_chex_pp_cd": { "title": "Current health expenditure per capita, PPP (current international $)", "description_from_producer": "Current expenditures on health per capita expressed in international dollars at purchasing power parity.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.CHEX.PP.CD (https://data.worldbank.org/indicator/SH.XPD.CHEX.PP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.CHEX.PP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current international $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current international $" } }, "sh_xpd_ehex_ch_zs": { "title": "External health expenditure (% of current health expenditure)", "description_from_producer": "Share of current health expenditures funded from external sources. External sources compose of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country. External sources either flow through the government scheme or are channeled through non-governmental organizations or other schemes.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.EHEX.CH.ZS (https://data.worldbank.org/indicator/SH.XPD.EHEX.CH.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.EHEX.CH.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of current health expenditure", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of current health expenditure" } }, "sh_xpd_ehex_pc_cd": { "title": "External health expenditure per capita (current US$)", "description_from_producer": "Current external expenditures on health per capita expressed in current US dollars. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.EHEX.PC.CD (https://data.worldbank.org/indicator/SH.XPD.EHEX.PC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.EHEX.PC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "sh_xpd_ehex_pp_cd": { "title": "External health expenditure per capita, PPP (current international $)", "description_from_producer": "Current external expenditures on health per capita expressed in international dollars at purchasing power parity. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.EHEX.PP.CD (https://data.worldbank.org/indicator/SH.XPD.EHEX.PP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.EHEX.PP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current international $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current international $" } }, "sh_xpd_ghed_ch_zs": { "title": "Domestic general government health expenditure (% of current health expenditure)", "description_from_producer": "Share of current health expenditures funded from domestic public sources for health. Domestic public sources include domestic revenue as internal transfers and grants, transfers, subsidies to voluntary health insurance beneficiaries, non-profit institutions serving households (NPISH) or enterprise financing schemes as well as compulsory prepayment and social health insurance contributions. They do not include external resources spent by governments on health.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.GHED.CH.ZS (https://data.worldbank.org/indicator/SH.XPD.GHED.CH.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.GHED.CH.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of current health expenditure", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of current health expenditure" } }, "sh_xpd_ghed_gd_zs": { "title": "Domestic general government health expenditure (% of GDP)", "description_from_producer": "Public expenditure on health from domestic sources as a share of the economy as measured by GDP.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.GHED.GD.ZS (https://data.worldbank.org/indicator/SH.XPD.GHED.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.GHED.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "sh_xpd_ghed_ge_zs": { "title": "Domestic general government health expenditure (% of general government expenditure)", "description_from_producer": "Public expenditure on health from domestic sources as a share of total public expenditure. It indicates the priority of the government to spend on health from own domestic public resources.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.GHED.GE.ZS (https://data.worldbank.org/indicator/SH.XPD.GHED.GE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.GHED.GE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of general government expenditure", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of general government expenditure" } }, "sh_xpd_ghed_pc_cd": { "title": "Domestic general government health expenditure per capita (current US$)", "description_from_producer": "Public expenditure on health from domestic sources per capita expressed in current US dollars.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.GHED.PC.CD (https://data.worldbank.org/indicator/SH.XPD.GHED.PC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.GHED.PC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "sh_xpd_ghed_pp_cd": { "title": "Domestic general government health expenditure per capita, PPP (current international $)", "description_from_producer": "Public expenditure on health from domestic sources per capita expressed in international dollars at purchasing power parity.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.GHED.PP.CD (https://data.worldbank.org/indicator/SH.XPD.GHED.PP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.GHED.PP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current international $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current international $" } }, "sh_xpd_oopc_ch_zs": { "title": "Out-of-pocket expenditure (% of current health expenditure)", "description_from_producer": "Share of out-of-pocket payments of total current health expenditures. Out-of-pocket payments are spending on health directly out-of-pocket by households.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. Aggregations are weighted by total current health expenditure (not by population).\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.OOPC.CH.ZS (https://data.worldbank.org/indicator/SH.XPD.OOPC.CH.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.OOPC.CH.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of current health expenditure", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of current health expenditure" } }, "sh_xpd_oopc_pc_cd": { "title": "Out-of-pocket expenditure per capita (current US$)", "description_from_producer": "Health expenditure through out-of-pocket payments per capita in USD. Out of pocket payments are spending on health directly out of pocket by households in each country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.OOPC.PC.CD (https://data.worldbank.org/indicator/SH.XPD.OOPC.PC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.OOPC.PC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "sh_xpd_oopc_pp_cd": { "title": "Out-of-pocket expenditure per capita, PPP (current international $)", "description_from_producer": "Health expenditure through out-of-pocket payments per capita in international dollars at purchasing power parity.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.OOPC.PP.CD (https://data.worldbank.org/indicator/SH.XPD.OOPC.PP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.OOPC.PP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current international $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current international $" } }, "sh_xpd_pvtd_ch_zs": { "title": "Domestic private health expenditure (% of current health expenditure)", "description_from_producer": "Share of current health expenditures funded from domestic private sources. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.PVTD.CH.ZS (https://data.worldbank.org/indicator/SH.XPD.PVTD.CH.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.PVTD.CH.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of current health expenditure", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of current health expenditure" } }, "sh_xpd_pvtd_pc_cd": { "title": "Domestic private health expenditure per capita (current US$)", "description_from_producer": "Current private expenditures on health per capita expressed in current US dollars. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.PVTD.PC.CD (https://data.worldbank.org/indicator/SH.XPD.PVTD.PC.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.PVTD.PC.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "sh_xpd_pvtd_pp_cd": { "title": "Domestic private health expenditure per capita, PPP (current international $)", "description_from_producer": "Current private expenditures on health per capita expressed in international dollars at purchasing power parity.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at .\nStatistical concept(s): The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).\n\n### Development relevance:\nStrengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c). The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources. Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8). The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.", "origins": [ { "producer": "Global Health Expenditure Database, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Expenditure Database, updated December 12th, 2025, World Health Organization (WHO), uri: http://apps.who.int/nha/database. Indicator SH.XPD.PVTD.PP.CD (https://data.worldbank.org/indicator/SH.XPD.PVTD.PP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH.XPD.PVTD.PP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current international $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current international $" } }, "sh_uhc_fh40": { "title": "Proportion of population facing financial hardship due to out-of-pocket health expenditure (%)", "description_from_producer": "Proportion of population spending more than 40% of household discretionary budget on out-of-pocket health expenditure (%)\n\n\n\nThe discretionary budget is defined as the total household budget, measured by consumption expenditure or income, minus the societal poverty line (SPL ). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health.\n\n\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Proportion of population facing financial hardship due to out-of-pocket health expenditure (%) is measured as the population-weighted average of the number of people living in households with out-of-pocket household expenditure on health exceeding 40% of household discretionary budget.\nStatistical concept(s): The household discretionary budget is defined as total household consumption expenditure or income minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. Households living in societal poverty, have a negative discretionary budget.\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home). This excludes any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Continued in \u201cOther notes.\u201d\n\n### Development relevance:\nFinancial hardship due to out-of-pocket (OOP) health spending reflects the extent to which people\u2019s direct health payments reduce their living standards. Because OOP health payments are made at the point of care without risk pooling, they include formal and informal payments for any health service or health product, OOP health payments can occur in any health system and affect people at any income level.\n\nFinancial hardship due to OOP health spending is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services, they need without compromising their ability to meet basic needs or significantly reducing their ability to consume other goods and services. As Sustainable Development Goal (SDG) indicator 3.8.2, financial hardship forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country\u2019s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development.\n\n### Other notes:\nThe proportion of population facing financial hardship due to out-of-pocket health expenditure can be decomposed into two mutually exclusive categories: the proportion of population with impoverishing out-of-pocket health expenditure and the proportion of the population with large but non-impoverishing out-of-pocket health expenditure\n(i) Impoverishing out-of-pocket health expenditure\nOut-of-pocket health expenditure is defined as impoverishing if it exceeds 100% of household discretionary budget. This is spending that leaves the household with no discretionary budget, or with inability to meet basic needs, once out-of-pocket health expenditure is subtracted from their discretionary budget. For households with negative discretionary budgets (those living in societal poverty), any OOP health spending exceeds 100% of the household discretionary budget.\n\nThe proportion of population incurring impoverishing out-of-pocket health expenditure can be further decomposed into the proportion of population further impoverished and the proportion pushed into poverty due to out-of-pocket health expenditure.\n\nOut-of-pocket health expenditure is defined as further impoverishing for households that live in societal poverty (household total consumption or income lies below the societal poverty line) and incur any out-of-pocket health expenditure.\n\nOut-of-pocket health expenditure is considered as pushing into poverty if households with positive discretionary budgets spend all their discretionary budget on out-of-pocket health expenditure. In other words, household total consumption (or income) is above the societal poverty line, but its total consumption (or income) net of out-of-pocket health expenditure falls below the societal poverty line, pushing the household into societal poverty.\n\n(ii) Large (but not impoverishing out-of-pocket) health spending\nOut-of-pocket health spending is defined as large (but non-impoverishing) if it exceeds 40% but remains below 100% of household discretionary budget. This implies ability to meet basic needs but a substantial reduction in ability to consume other goods and services due to the need to pay for health care.\nThe recommended data sources for monitoring this indicator are household budget surveys and household income and expenditure surveys, which provide information on both household consumption expenditure on health and total household consumption expenditures, and are routinely conducted by National Statistical Offices (NSOs). Preference is given for the use of consumption expenditure as a measure or household budget and discretionary budget, with income being used when consumption expenditure data is unavailable.\n\nTo compute regional and global aggregates for a common reference year, survey-based country estimates are first \u201clined-up\u201d using a combination of interpolation/extrapolation (when there are at least two survey-based estimates available within 5 years around the reference year); econometric modelling (one survey-based estimate available around the reference year); and imputation based on income group medians (no survey-based estimates available around the reference year) (see also SDG 3.8.2 metadata description (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf), sections 4.f and 4.g).", "origins": [ { "producer": "Universal Health Coverage Dataset, WHO and World Bank, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Universal Health Coverage Dataset, World Health Organization (WHO);\nWorld Bank (WB), note: Universal Health Coverage Dataset. Indicator SH_UHC_FH40 (https://data.worldbank.org/indicator/SH_UHC_FH40). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH_UHC_FH40", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_uhc_fh40_further": { "title": "Proportion of population further impoverished due to out-of-pocket health expenditure, based on the societal poverty line (%)", "description_from_producer": "Proportion of population living in households already in societal poverty (household total budget lies below the societal poverty line) that incur any out-of-pocket health expenditure.\n\nThe household discretionary budget is defined as the total household budget, measured by consumption expenditure or income, minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health.\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Proportion of population further impoverished due to out-of-pocket health expenditure (%) is measured as the population-weighted average of the number of people living in households in societal poverty (household total consumption or income lies below the societal poverty line) that incur any out-of-pocket expenditure\nStatistical concept(s): The household discretionary budget is defined as total household consumption expenditure or income minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. Households living in societal poverty, have a negative discretionary budget.\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home). This excludes any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Continued in \u201cOther notes.\u201d\n\n### Development relevance:\nFinancial hardship due to out-of-pocket (OOP) health spending reflects the extent to which people\u2019s direct health payments reduce their living standards. Because OOP health payments are made at the point of care without risk pooling, they include formal and informal payments for any health service or health product, OOP health payments can occur in any health system and affect people at any income level.\n\nFinancial hardship due to OOP health spending is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services, they need without compromising their ability to meet basic needs or significantly reducing their ability to consume other goods and services. As Sustainable Development Goal (SDG) indicator 3.8.2, financial hardship forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country\u2019s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development.\n\n### Other notes:\nThe proportion of population facing financial hardship due to out-of-pocket health expenditure can be decomposed into two mutually exclusive categories: the proportion of population with impoverishing out-of-pocket health expenditure and the proportion of the population with large but non-impoverishing out-of-pocket health expenditure\n(i) Impoverishing out-of-pocket health expenditure\nOut-of-pocket health expenditure is defined as impoverishing if it exceeds 100% of household discretionary budget. This is spending that leaves the household with no discretionary budget, or with inability to meet basic needs, once out-of-pocket health expenditure is subtracted from their discretionary budget. For households with negative discretionary budgets (those living in societal poverty), any OOP health spending exceeds 100% of the household discretionary budget.\nThe proportion of population incurring impoverishing out-of-pocket health expenditure can be further decomposed into the proportion of population further impoverished and the proportion pushed into poverty due to out-of-pocket health expenditure.\n\nOut-of-pocket health expenditure is defined as further impoverishing for households that live in societal poverty (household total consumption or income lies below the societal poverty line) and incur any out-of-pocket health expenditure.\n\nOut-of-pocket health expenditure is considered as pushing into poverty if households with positive discretionary budgets spend all their discretionary budget on out-of-pocket health expenditure. In other words, household total consumption (or income) is above the societal poverty line, but its total consumption (or income) net of out-of-pocket health expenditure falls below the societal poverty line, pushing the household into societal poverty.\n\n(ii) Large (but not impoverishing out-of-pocket) health spending\nOut-of-pocket health spending is defined as large (but non-impoverishing) if it exceeds 40% but remains below 100% of household discretionary budget. This implies ability to meet basic needs but a substantial reduction in ability to consume other goods and services due to the need to pay for health care.\n\nThe recommended data sources for monitoring this indicator are household budget surveys and household income and expenditure surveys, which provide information on both household consumption expenditure on health and total household consumption expenditures, and are routinely conducted by National Statistical Offices (NSOs). Preference is given for the use of consumption expenditure as a measure or household budget and discretionary budget, with income being used when consumption expenditure data is unavailable.\n\nTo compute regional and global aggregates for a common reference year, survey-based country estimates are first \u201clined-up\u201d using a combination of interpolation/extrapolation (when there are at least two survey-based estimates available within 5 years around the reference year); econometric modelling (one survey-based estimate available around the reference year); and imputation based on income group medians (no survey-based estimates available around the reference year) (see also SDG 3.8.2 metadata description (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf), sections 4.f and 4.g).", "origins": [ { "producer": "Universal Health Coverage Dataset, WHO and World Bank, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Universal Health Coverage Dataset, World Health Organization (WHO);\nWorld Bank (WB), note: Universal Health Coverage Dataset. Indicator SH_UHC_FH40_FURTHER (https://data.worldbank.org/indicator/SH_UHC_FH40_FURTHER). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH_UHC_FH40_FURTHER", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_uhc_fh40_impov": { "title": "Proportion of population facing impoverishing out-of-pocket health expenditure, based on the societal poverty line (%)", "description_from_producer": "Proportion of population spending more than 100% of household discretionary budget on out-of-pocket health expenditure (%). This includes people living in poverty that are further impoverished due to out-of-pocket health expenditure as well as people who are pushed into poverty by out-of-pocket health expenditure.\n\nThe discretionary budget is defined as the total household budget, measured by consumption expenditure or income, minus the societal poverty line (SPL ). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health.\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Proportion of population facing impoverishing out-of-pocket health expenditure (%) is measured as the population-weighted average of the number of people living in households with positive out-of-pocket household expenditure on health exceeding 100% of household discretionary budget.\nStatistical concept(s): The household discretionary budget is defined as total household consumption expenditure or income minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. Households living in societal poverty, have a negative discretionary budget.\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home). This excludes any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Continued in \u201cOther notes.\u201d\n\n### Development relevance:\nFinancial hardship due to out-of-pocket health spending reflects the extent to which people\u2019s direct health payments reduce their living standards. Because OOP health payments are made at the point of care without risk pooling, they include formal and informal payments for any health service or health product, OOP health payments can occur in any health system and affect people at any income level.\n\nFinancial hardship due to OOP health spending is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services, they need without compromising their ability to meet basic needs or significantly reducing their ability to consume other goods and services. As Sustainable Development Goal (SDG) indicator 3.8.2, financial hardship forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country\u2019s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development.\n\n### Other notes:\nThe proportion of population facing financial hardship due to out-of-pocket health expenditure can be decomposed into two mutually exclusive categories: the proportion of population with impoverishing out-of-pocket health expenditure and the proportion of the population with large but non-impoverishing out-of-pocket health expenditure\n(i) Impoverishing out-of-pocket health expenditure\nOut-of-pocket health expenditure is defined as impoverishing if it exceeds 100% of household discretionary budget. This is spending that leaves the household with no discretionary budget, or with inability to meet basic needs, once out-of-pocket health expenditure is subtracted from their discretionary budget. For households with negative discretionary budgets (those living in societal poverty), any OOP health spending exceeds 100% of the household discretionary budget.\nThe proportion of population incurring impoverishing out-of-pocket health expenditure can be further decomposed into the proportion of population further impoverished and the proportion pushed into poverty due to out-of-pocket health expenditure.\n\nOut-of-pocket health expenditure is defined as further impoverishing for households that live in societal poverty (household total consumption or income lies below the societal poverty line) and incur any out-of-pocket health expenditure.\n\nOut-of-pocket health expenditure is considered as pushing into poverty if households with positive discretionary budgets spend all their discretionary budget on out-of-pocket health expenditure. In other words, household total consumption (or income) is above the societal poverty line, but its total consumption (or income) net of out-of-pocket health expenditure falls below the societal poverty line, pushing the household into societal poverty.\n\n(ii) Large (but not impoverishing out-of-pocket) health spending\nOut-of-pocket health spending is defined as large (but non-impoverishing) if it exceeds 40% but remains below 100% of household discretionary budget. This implies ability to meet basic needs but a substantial reduction in ability to consume other goods and services due to the need to pay for health care.\n\nThe recommended data sources for monitoring this indicator are household budget surveys and household income and expenditure surveys, which provide information on both household consumption expenditure on health and total household consumption expenditures, and are routinely conducted by National Statistical Offices (NSOs). Preference is given for the use of consumption expenditure as a measure or household budget and discretionary budget, with income being used when consumption expenditure data is unavailable.\n\nTo compute regional and global aggregates for a common reference year, survey-based country estimates are first \u201clined-up\u201d using a combination of interpolation/extrapolation (when there are at least two survey-based estimates available within 5 years around the reference year); econometric modelling (one survey-based estimate available around the reference year); and imputation based on income group medians (no survey-based estimates available around the reference year) (see also SDG 3.8.2 metadata description (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf), sections 4.f and 4.g).", "origins": [ { "producer": "Universal Health Coverage Dataset, WHO and World Bank, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Universal Health Coverage Dataset, World Health Organization (WHO);\nWorld Bank (WB), note: Universal Health Coverage Dataset. Indicator SH_UHC_FH40_IMPOV (https://data.worldbank.org/indicator/SH_UHC_FH40_IMPOV). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH_UHC_FH40_IMPOV", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_uhc_fh40_large": { "title": "Proportion of population facing large (but non-impoverishing) out-of-pocket health expenditure (%)", "description_from_producer": "Proportion of population spending more than 40% but less than 100% of household discretionary budget on out-of-pocket health expenditure (%)\n\n\n\nThe discretionary budget is defined as the total household budget, measured by consumption expenditure or income, minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health.\n\n\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Proportion of population facing large (but non-impoverishing) out-of-pocket health expenditure (%) is measured as the population-weighted average of the number of people living in households with positive out-of-pocket household expenditure on health exceeding 40% but less than 100% of household discretionary budget.\nStatistical concept(s): The household discretionary budget is defined as total household consumption expenditure or income minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. Households living in societal poverty, have a negative discretionary budget.\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home). This excludes any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Continued in \u201cOther notes.\u201d\n\n### Development relevance:\nFinancial hardship due to out-of-pocket (OOP) health spending reflects the extent to which people\u2019s direct health payments reduce their living standards. Because OOP health payments are made at the point of care without risk pooling, they include formal and informal payments for any health service or health product, OOP health payments can occur in any health system and affect people at any income level.\n\nFinancial hardship due to OOP health spending is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services, they need without compromising their ability to meet basic needs or significantly reducing their ability to consume other goods and services. As Sustainable Development Goal (SDG) indicator 3.8.2, financial hardship forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country\u2019s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development.\n\n### Other notes:\nThe proportion of population facing financial hardship due to out-of-pocket health expenditure can be decomposed into two mutually exclusive categories: the proportion of population with impoverishing out-of-pocket health expenditure and the proportion of the population with large but non-impoverishing out-of-pocket health expenditure\n(i) Impoverishing out-of-pocket health expenditure\nOut-of-pocket health expenditure is defined as impoverishing if it exceeds 100% of household discretionary budget. This is spending that leaves the household with no discretionary budget, or with inability to meet basic needs, once out-of-pocket health expenditure is subtracted from their discretionary budget. For households with negative discretionary budgets (those living in societal poverty), any OOP health spending exceeds 100% of the household discretionary budget.\nThe proportion of population incurring impoverishing out-of-pocket health expenditure can be further decomposed into the proportion of population further impoverished and the proportion pushed into poverty due to out-of-pocket health expenditure.\n\nOut-of-pocket health expenditure is defined as further impoverishing for households that live in societal poverty (household total consumption or income lies below the societal poverty line) and incur any out-of-pocket health expenditure.\n\nOut-of-pocket health expenditure is considered as pushing into poverty if households with positive discretionary budgets spend all their discretionary budget on out-of-pocket health expenditure. In other words, household total consumption (or income) is above the societal poverty line, but its total consumption (or income) net of out-of-pocket health expenditure falls below the societal poverty line, pushing the household into societal poverty.\n\n(ii) Large (but not impoverishing out-of-pocket) health spending\nOut-of-pocket health spending is defined as large (but non-impoverishing) if it exceeds 40% but remains below 100% of household discretionary budget. This implies ability to meet basic needs but a substantial reduction in ability to consume other goods and services due to the need to pay for health care.\n\nThe recommended data sources for monitoring this indicator are household budget surveys and household income and expenditure surveys, which provide information on both household consumption expenditure on health and total household consumption expenditures, and are routinely conducted by National Statistical Offices (NSOs). Preference is given for the use of consumption expenditure as a measure or household budget and discretionary budget, with income being used when consumption expenditure data is unavailable.\n\nTo compute regional and global aggregates for a common reference year, survey-based country estimates are first \u201clined-up\u201d using a combination of interpolation/extrapolation (when there are at least two survey-based estimates available within 5 years around the reference year); econometric modelling (one survey-based estimate available around the reference year); and imputation based on income group medians (no survey-based estimates available around the reference year) (see also SDG 3.8.2 metadata description (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf), sections 4.f and 4.g).", "origins": [ { "producer": "Universal Health Coverage Dataset, WHO and World Bank, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Universal Health Coverage Dataset, World Health Organization (WHO);\nWorld Bank (WB), note: World Health Organization ( WHO ). Indicator SH_UHC_FH40_LARGE (https://data.worldbank.org/indicator/SH_UHC_FH40_LARGE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH_UHC_FH40_LARGE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_uhc_fh40_pushed": { "title": "Proportion of population pushed into poverty (based on the societal poverty line) due to out-of-pocket health expenditure (%)", "description_from_producer": "Proportion of population living in households with positive discretionary budgets that spend all their discretionary budget on out-of-pocket health expenditure.\n\nThe household discretionary budget is defined as the total household budget, measured by consumption expenditure or income, minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health.\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Proportion of population pushed into poverty due to out-of-pocket health expenditure (%) is measured as the population-weighted average of the number of people living in households with positive discretionary budgets that spend all their discretionary budget on out-of-pocket health expenditure.\nStatistical concept(s): The household discretionary budget is defined as total household consumption expenditure or income minus the societal poverty line (SPL). Using 2017 purchasing power parities (PPPs), the SPL corresponds to whichever is greater: $2.15 (the international poverty line) or $1.15 + 50% of median household consumption expenditure or income, excluding out-of-pocket household expenditure on health. Households living in societal poverty, have a negative discretionary budget.\n\nOut-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home). This excludes any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Continued in \u201cOther notes.\u201d\n\n### Development relevance:\nFinancial hardship due to out-of-pocket (OOP) health spending reflects the extent to which people\u2019s direct health payments reduce their living standards. Because OOP health payments are made at the point of care without risk pooling, they include formal and informal payments for any health service or health product, OOP health payments can occur in any health system and affect people at any income level.\n\nFinancial hardship due to OOP health spending is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services, they need without compromising their ability to meet basic needs or significantly reducing their ability to consume other goods and services. As Sustainable Development Goal (SDG) indicator 3.8.2, financial hardship forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country\u2019s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development.\n\n### Other notes:\nThe proportion of population facing financial hardship due to out-of-pocket health expenditure can be decomposed into two mutually exclusive categories: the proportion of population with impoverishing out-of-pocket health expenditure and the proportion of the population with large but non-impoverishing out-of-pocket health expenditure\n(i) Impoverishing out-of-pocket health expenditure\nOut-of-pocket health expenditure is defined as impoverishing if it exceeds 100% of household discretionary budget. This is spending that leaves the household with no discretionary budget, or with inability to meet basic needs, once out-of-pocket health expenditure is subtracted from their discretionary budget. For households with negative discretionary budgets (those living in societal poverty), any OOP health spending exceeds 100% of the household discretionary budget.\nThe proportion of population incurring impoverishing out-of-pocket health expenditure can be further decomposed into the proportion of population further impoverished and the proportion pushed into poverty due to out-of-pocket health expenditure.\n\nOut-of-pocket health expenditure is defined as further impoverishing for households that live in societal poverty (household total consumption or income lies below the societal poverty line) and incur any out-of-pocket health expenditure.\n\nOut-of-pocket health expenditure is considered as pushing into poverty if households with positive discretionary budgets spend all their discretionary budget on out-of-pocket health expenditure. In other words, household total consumption (or income) is above the societal poverty line, but its total consumption (or income) net of out-of-pocket health expenditure falls below the societal poverty line, pushing the household into societal poverty.\n\n(ii) Large (but not impoverishing out-of-pocket) health spending\nOut-of-pocket health spending is defined as large (but non-impoverishing) if it exceeds 40% but remains below 100% of household discretionary budget. This implies ability to meet basic needs but a substantial reduction in ability to consume other goods and services due to the need to pay for health care.\n\nThe recommended data sources for monitoring this indicator are household budget surveys and household income and expenditure surveys, which provide information on both household consumption expenditure on health and total household consumption expenditures, and are routinely conducted by National Statistical Offices (NSOs). Preference is given for the use of consumption expenditure as a measure or household budget and discretionary budget, with income being used when consumption expenditure data is unavailable.\n\nTo compute regional and global aggregates for a common reference year, survey-based country estimates are first \u201clined-up\u201d using a combination of interpolation/extrapolation (when there are at least two survey-based estimates available within 5 years around the reference year); econometric modelling (one survey-based estimate available around the reference year); and imputation based on income group medians (no survey-based estimates available around the reference year) (see also SDG 3.8.2 metadata description (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-02.pdf), sections 4.f and 4.g).", "origins": [ { "producer": "Universal Health Coverage Dataset, WHO and World Bank, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Universal Health Coverage Dataset, World Health Organization (WHO);\nWorld Bank (WB), note: Universal Health Coverage Dataset. Indicator SH_UHC_FH40_PUSHED (https://data.worldbank.org/indicator/SH_UHC_FH40_PUSHED). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH_UHC_FH40_PUSHED", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_uhc_sci": { "title": "UHC service coverage index", "description_from_producer": "A composite index representing coverage of essential health services based on 14 tracer indicators in the areas of reproductive, maternal, newborn and child health, infectious diseases, noncommunicable diseases and service capacity and access. It is presented on a scale of 0 to 100 index points.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The UHC service coverage index is computed as the weighted geometric means of 14 tracer indicators, as follows, organized by four broad categories of service coverage:\nI. Reproductive, maternal, newborn and child health\n1. Family planning: Percentage of women of reproductive age (15-49 years) who have their need for family planning satisfied with modern methods (SDG indicator 3.7.1, metadata available at https://unstats.un.org/sdgs/metadata/files/Metadata-03-07-01.pdf)\n2. Pregnancy care: Percentage of women aged 15-49 years with a live birth in a given time period who received antenatal care four or more times\n3. Child immunization: Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine\n4. Child treatment: Percentage of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing due to a problem in the chest and not due to a blocked nose only) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider\nII. Infectious diseases\n5. Tuberculosis: Percentage of incident TB cases that are detected and treated\n6. HIV/AIDS: Percentage of adults and children living with HIV currently receiving antiretroviral therapy\n7. Malaria: Percentage of population in malaria-endemic areas who slept under an insecticide-treated net the previous night [only for countries with high malaria burden]\n8. Water, sanitation and hygiene: Percentage of population using at least basic sanitation services.\nIII. Noncommunicable diseases\n9. Hypertension: Coverage of treatment (taking medicine) for hypertension among adults aged 30-79 years with hypertension (age-standardized estimate) (%)\n10. Diabetes: Coverage of treatment (taking medication) for diabetes among adults aged 30 years and over with diabetes (age-standardized estimate) (%)\n11. Tobacco: Age-standardized prevalence of adults >=15 years currently using any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis (SDG indicator 3.a.1, metadata available at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0a-01.pdf)\nIV. Service capacity and access\n12. Hospital access: Hospital beds density, relative to a maximum threshold of 18 per 10,000 population?\n13. Health workforce: Health workers (medical doctors, nursing and midwifery personnel) per capita, relative to a combined maximum thresholds (overlap with SDG indicator 3.c.1, see metadata at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0C-01.pdf)\n14. Health security: International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of 13 core capacities that have been attained?(SDG indicator 3.d.1, see metadata at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0D-01.pdf)\nRefer to Other notes for the Statistical Concept(s).\n\n### Development relevance:\nSDG Target 3.8 is defined as \u201cAchieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all\u201d. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1, the universal health service coverage index, is for service coverage of essential health services while indicator 3.8.2 focuses on financial hardship caused by out-of-pocket health expenditure.\n\nIndicators of service coverage \u2013 defined as people receiving the service they need \u2013 are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, the UHC service coverage index is constructed from?14?tracer indicators (further organized into 4 health service areas) selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden.?The index is reported on a?unitless?scale of 0 to 100, with 100 being the?optimal?value.\n\n### Other notes:\nStatistical concept(s): The calculation the UHC service coverage index requires first standardizing the 14 tracer indicators so that they can be combined into the index, and then computing the index from those values.\nThe 14 tracer indicators are first all placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement, e.g., the percentage of infants who have been immunized ranges from 0 to 100 percent. However, for a few indicators, conversion and/or rescaling is required to obtain appropriate values from 0 to 100. Once all tracer indicator values are on a scale of 0 to 100, weighted geometric means are computed within each of the four health service areas, and then a geometric mean is taken of those four values. Each tracer is weighted on an indicator specific population based on the denominator of the indicator using United Nations population estimates where applicable.\nMany of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in Annex 1 (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf)", "origins": [ { "producer": "Universal Health Coverage Dataset, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Universal Health Coverage Dataset, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/uhc-index-of-service-coverage. Indicator SH_UHC_SCI (https://data.worldbank.org/indicator/SH_UHC_SCI). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH_UHC_SCI", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_uhc_sci_capacity": { "title": "UHC service coverage sub-index on service capacity and access", "description_from_producer": "UHC service coverage sub-index on service capacity and access is based on three tracer indicators (i) hospital access, (ii) health workforce, (iii) health security. It is presented on a scale of 0 to 100 index points and is one of the four sub-indexes underlaying the overall UHC service coverage index.\n\n### Aggregation method:\nWeighted geometric mean\n\n### Statistical concept and methodology:\nMethodology: The UHC service coverage index for service capacity and access is computed as the weighted geometric mean of the following three tracer indicators: (i) hospital access, (ii) health workforce, (iii) health security\nStatistical concept(s): The UHC service coverage sub-index on service capacity and access is one of the four sub-indexes underlaying the overall UHC service coverage index. It is computed as the weighted geometric mean of the following:\n1.?Hospital access: Hospital beds density, relative to a maximum threshold of 18 per 10,000 population?\n2.?Health workforce:?Health workers (medical doctors, nursing and midwifery personnel) per capita, relative to a combined maximum thresholds?(overlap with?SDG indicator 3.c.1, see metadata at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0C-01.pdf)?\n3.?Health security:?International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of 13 core capacities that have been attained?(SDG indicator 3.d.1, see metadata?at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0D-01.pdf)?\nAll tracer indicators are first placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement. However, for a few indicators, conversion and/or rescaling is required?to obtain?appropriate?values from 0 to 100. Once all?tracer indicator values?are on?a scale of 0 to 100, weighted geometric means are computed.? Each tracer indicator is weighted on an indicator specific population based on the denominator of the indicator using United Nations population estimates where applicable.\n\nMany of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in Annex 1 (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf)\n\n### Development relevance:\nSDG Target 3.8 is defined as \u201cAchieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all\u201d. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1, the universal health service coverage index, is for service coverage of essential health services while indicator 3.8.2 focuses on financial hardship caused by out-of-pocket health expenditure.\n\nIndicators of service coverage \u2013 defined as people receiving the service they need \u2013 are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, the UHC service coverage index is constructed from?14?tracer indicators (further organized into 4 health service areas) selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden.?The index is reported on a?unitless?scale of 0 to 100, with 100 being the?optimal?value.", "origins": [ { "producer": "Universal Health Coverage Dataset, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Universal Health Coverage Dataset, World Health Organization (WHO) [WHO], uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/uhc-index-of-service-coverage. Indicator SH_UHC_SCI_CAPACITY (https://data.worldbank.org/indicator/SH_UHC_SCI_CAPACITY). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH_UHC_SCI_CAPACITY", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_uhc_sci_id": { "title": "UHC service coverage sub-index on infectious diseases", "description_from_producer": "UHC service coverage sub-index on infectious diseases is based on four tracer indicators: (i) Tuberculosis, (ii) HIV/AIDS (iii) Malaria (iv) Water, sanitation and hygiene. It is presented on a scale of 0 to 100 index points and is one of the four sub-indexes underlying the overall UHC service coverage index.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The UHC service coverage sub-index on infectious diseases is computed as the weighted geometric mean of the following four tracer indicators: (i) Tuberculosis, (ii) HIV/AIDS (iii) Malaria (iv) Water, sanitation and hygiene\nStatistical concept(s): The UHC service coverage sub-index on infectious diseases is one of the four sub-indexes underlying the overall UHC service coverage index. It is computed as the weighted geometric mean of the following:\n1.?Tuberculosis:?Percentage of incident?TB cases that are detected and treated?\n2.?HIV/AIDS:?Percentage of adults and children living with HIV currently receiving antiretroviral therapy?\n3.?Malaria:?Percentage of population in malaria-endemic areas who slept under an?insecticide-treated net?the previous night [only for countries with high malaria burden]?\n4.?Water, sanitation and hygiene:?Percentage of population using?at least basic?sanitation services.?\n\nAll tracer indicators are first placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement. However, for a few indicators, conversion and/or rescaling is required?to obtain?appropriate?values from 0 to 100. Once all?tracer indicator values?are on?a scale of 0 to 100, weighted geometric means are computed. Each tracer is weighted on an indicator specific population based on the denominator of the indicator using United Nations population estimates where applicable.\n\nMany of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in Annex 1 (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf)\n\n### Development relevance:\nSDG Target 3.8 is defined as \u201cAchieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all\u201d. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1, the universal health service coverage index, is for service coverage of essential health services while indicator 3.8.2 focuses on financial hardship caused by out-of-pocket health expenditure.\n\nIndicators of service coverage \u2013 defined as people receiving the service they need \u2013 are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, the UHC service coverage index is constructed from?14?tracer indicators (further organized into 4 health service areas) selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden.?The index is reported on a?unitless?scale of 0 to 100, with 100 being the?optimal?value.", "origins": [ { "producer": "Universal Health Coverage Dataset, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Universal Health Coverage Dataset, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/uhc-index-of-service-coverage. Indicator SH_UHC_SCI_ID (https://data.worldbank.org/indicator/SH_UHC_SCI_ID). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH_UHC_SCI_ID", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_uhc_sci_ncd": { "title": "UHC service coverage sub-index on non-communicable diseases", "description_from_producer": "UHC service coverage sub-index on non-communicable diseases is based on three tracer indicators: (i) hypertension, (ii) diabetes, (iii) tobacco. It is presented on a scale of 0 to 100 index points and is one of the four sub-indexes underlying the overall UHC service coverage index.\n\n### Aggregation method:\nWeighted geometric mean\n\n### Statistical concept and methodology:\nMethodology: The UHC service coverage sub-index on non-communicable diseases is computed as the weighted geometric mean of the following three tracer indicators: (i) hypertension, (ii) diabetes, (iii) tobacco\nStatistical concept(s): The UHC service coverage sub-index on non-communicable diseases is one of the four sub-indexes underlying the overall UHC service coverage index. It is computed as the weighted geometric mean of the following:\n1.?Hypertension:?Coverage of treatment (taking medicine) for hypertension among adults aged 30-79 years with hypertension (age-standardized estimate) (%)\n2.?Diabetes:? Coverage of treatment (taking medication) for diabetes among adults aged 30 years and over with diabetes (age-standardized estimate) (%)\n3.?Tobacco:?Age-standardized prevalence of adults >=15 years currently using any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis?(SDG indicator?3.a.1, metadata available at https://unstats.un.org/sdgs/metadata/files/Metadata-03-0a-01.pdf)?\n\nAll tracer indicators are first placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement. However, for a few indicators, conversion and/or rescaling is required?to obtain?appropriate?values from 0 to 100. Once all?tracer indicator values?are on?a scale of 0 to 100, weighted geometric means are computed. Each tracer indicator is weighted on an indicator specific population based on the denominator of the indicator using United Nations population estimates where applicable.\n\nMany of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in Annex 1 (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf)\n\n### Development relevance:\nSDG Target 3.8 is defined as \u201cAchieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all\u201d. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1, the universal health service coverage index, is for service coverage of essential health services while indicator 3.8.2 focuses on financial hardship caused by out-of-pocket health expenditure.\n\nIndicators of service coverage \u2013 defined as people receiving the service they need \u2013 are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, the UHC service coverage index is constructed from?14?tracer indicators (further organized into 4 health service areas) selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden.?The index is reported on a?unitless?scale of 0 to 100, with 100 being the?optimal?value.", "origins": [ { "producer": "Universal Health Coverage Dataset, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Universal Health Coverage Dataset, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/uhc-index-of-service-coverage. Indicator SH_UHC_SCI_NCD (https://data.worldbank.org/indicator/SH_UHC_SCI_NCD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH_UHC_SCI_NCD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sh_uhc_sci_rmnch": { "title": "UHC service coverage sub-index on reproductive, maternal, newborn and child health", "description_from_producer": "UHC service coverage sub-index on reproductive, maternal,?newborn?and child health services is based on four tracer indicators: (i) family planning, (ii) pregnancy care, (iii) child immunization, (iv) child treatment. It is presented on a scale of 0 to 100 index points and is one of the four sub-indexes underlaying the overall UHC service coverage index.\n\n### Aggregation method:\nWeighted geometric mean\n\n### Statistical concept and methodology:\nMethodology: The UHC service coverage sub-index on reproductive, maternal,?newborn?and child health services?is computed as the weighted geometric mean of the following four tracer indicators: (i) family planning, (ii) pregnancy care, (iii) child immunization, (iv) child treatment\nStatistical concept(s): The UHC service coverage sub-index on reproductive, maternal, newborn, and child health services is one of the four sub-indexes underlaying the overall UHC service coverage index. It is computed as the weighted geometric mean of the following:\n1.?Family planning:?Percentage of women of reproductive age (15-49 years) who have their need for family planning satisfied with modern methods? (SDG indicator?3.7.1, metadata available?at https://unstats.un.org/sdgs/metadata/files/Metadata-03-07-01.pdf)?\n2.?Pregnancy care:?Percentage of women aged 15-49 years with a live birth?in a given?time period who received antenatal care four or more times?\n3.?Child immunization:?Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine?\n4.?Child treatment:?Percentage of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing due to a problem in the chest and not due to a blocked nose only) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider\n\nAll tracer indicators are first placed on the same scale, with 0 being the lowest value and 100 being the optimal value. For most indicators, this scale is the natural scale of measurement. However, for a few indicators, conversion and/or rescaling is required?to obtain?appropriate?values from 0 to 100. Once all?tracer indicator values?are on?a scale of 0 to 100, weighted geometric means are computed.?Each tracer is weighted on an indicator specific population based on the denominator of the indicator using United Nations population estimates where applicable.\n\nMany of the tracer indicators of health service coverage are measured by household surveys. However, administrative data, facility data, facility surveys, and sentinel surveillance systems are utilized for certain indicators. Underlying data sources for each of the 14 tracer indicators are explained in Annex 1 (https://unstats.un.org/sdgs/metadata/files/Metadata-03-08-01.pdf).\n\n### Development relevance:\nSDG Target 3.8 is defined as \u201cAchieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all\u201d. The objective is for all people and communities to receive the quality health services they need (including medicines and other health products), without financial hardship. Two indicators have been chosen to monitor target 3.8 within the SDG framework. Indicator 3.8.1, the universal health service coverage index, is for service coverage of essential health services while indicator 3.8.2 focuses on financial hardship caused by out-of-pocket health expenditure.\n\nIndicators of service coverage \u2013 defined as people receiving the service they need \u2013 are the best way to track progress in providing services under universal health coverage (UHC). Since a single health service indicator does not suffice for monitoring UHC, the UHC service coverage index is constructed from?14?tracer indicators (further organized into 4 health service areas) selected based on epidemiological and statistical criteria. This includes several indicators that are already included in other SDG targets, thereby minimizing the data collection and reporting burden.?The index is reported on a?unitless?scale of 0 to 100, with 100 being the?optimal?value.", "origins": [ { "producer": "Universal Health Coverage Dataset, WHO, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Universal Health Coverage Dataset, World Health Organization (WHO), uri: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/uhc-index-of-service-coverage. Indicator SH_UHC_SCI_RMNCH (https://data.worldbank.org/indicator/SH_UHC_SCI_RMNCH). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SH_UHC_SCI_RMNCH", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "si_dst_02nd_20": { "title": "Income share held by second 20%", "description_from_producer": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.\n\n### Statistical concept and methodology:\nMethodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.\n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the second quintile, meaning the second 20% of people when ranked from lowest to highest income.\n\n### Development relevance:\nIt measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.DST.02ND.20 (https://data.worldbank.org/indicator/SI.DST.02ND.20). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.DST.02ND.20", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_dst_03rd_20": { "title": "Income share held by third 20%", "description_from_producer": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.\n\n### Statistical concept and methodology:\nMethodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.\n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the third quintile, meaning the third 20% of people when ranked from lowest to highest income.\n\n### Development relevance:\nIt measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.DST.03RD.20 (https://data.worldbank.org/indicator/SI.DST.03RD.20). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.DST.03RD.20", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_dst_04th_20": { "title": "Income share held by fourth 20%", "description_from_producer": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.\n\n### Statistical concept and methodology:\nMethodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.\n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the fourth quintile, meaning the fourth 20% of people when ranked from lowest to highest income.\n\n### Development relevance:\nIt measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.DST.04TH.20 (https://data.worldbank.org/indicator/SI.DST.04TH.20). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.DST.04TH.20", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_dst_05th_20": { "title": "Income share held by highest 20%", "description_from_producer": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.\n\n### Statistical concept and methodology:\nMethodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.\n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the fifth or top quintile, meaning the top 20% of people when ranked from lowest to highest income.\n\n### Development relevance:\nIt measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.DST.05TH.20 (https://data.worldbank.org/indicator/SI.DST.05TH.20). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.DST.05TH.20", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_dst_10th_10": { "title": "Income share held by highest 10%", "description_from_producer": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.\n\n### Statistical concept and methodology:\nMethodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.\n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\nStatistical concept(s): The percentage of total income in a population that is held by the tenth or top decile, meaning the top 10% of people when ranked from lowest to highest income.\n\n### Development relevance:\nIt measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.DST.10TH.10 (https://data.worldbank.org/indicator/SI.DST.10TH.10). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.DST.10TH.10", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_dst_50md": { "title": "Proportion of people living below 50 percent of median income (%)", "description_from_producer": "The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2021 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.\n\n### Statistical concept and methodology:\nMethodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.\n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of a population whose income or consumption falls below half of the median in their country, essentially indicating the level of \"relative poverty\" within a society. It reflects the share of the population whose income or consumption is less than half of the typical standard in their society, highlighting income inequality at the lower end of the distribution.\n\n### Development relevance:\nIt measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.DST.50MD (https://data.worldbank.org/indicator/SI.DST.50MD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.DST.50MD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "si_dst_frst_10": { "title": "Income share held by lowest 10%", "description_from_producer": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.\n\n### Statistical concept and methodology:\nMethodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. No adjustment has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middle-income economies, see Ravallion and Chen (1996).\n\n\n\n\n\n\n\nSurvey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected.\nStatistical concept(s): The percentage of total income in a population that is held by the tenth or top decile, meaning the top 10% of people when ranked from lowest to highest income.\n\n### Development relevance:\nIt measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.DST.FRST.10 (https://data.worldbank.org/indicator/SI.DST.FRST.10). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.DST.FRST.10", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_dst_frst_20": { "title": "Income share held by lowest 20%", "description_from_producer": "Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.\n\n### Statistical concept and methodology:\nMethodology: Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.\n\n\n\n\n\n\n\nData on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.\n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\n\n\n\n\n\n\n\nPercentage shares by quintile may not sum to 100 because of rounding.\nStatistical concept(s): The percentage of total income in a population that is held by the bottom quintile, meaning the bottom 20% of people when ranked from lowest to highest income.\n\n### Development relevance:\nIt measures the level of inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.DST.FRST.20 (https://data.worldbank.org/indicator/SI.DST.FRST.20). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.DST.FRST.20", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_pov_dday": { "title": "Poverty headcount ratio at $3.00 a day (2021 PPP) (% of population)", "description_from_producer": "Poverty headcount ratio at $3.00 a day is the percentage of the population living on less than $3.00 a day at 2021 purchasing power adjusted prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\n\n\n\n\n\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\n\n\n\n\n\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n\n\n\n\n\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): Poverty headcount ratio at $3.00 a day refers to the percentage of a population whose consumption or income per day falls short of the international poverty line of $3.00 a day (adjusted for purchasing power parity differences across countries), the poverty line typical of low-income countries.\n\n### Development relevance:\nThe World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. The World Bank defines extreme poverty as living on less than $3.00 a day (adjusted for purchasing power differences across countries). The value of $3.00 is the typical poverty line of low-income countries, which is the minimum amount of money people in low-income countries need to cover their daily basic needs, including food, clothing, and shelter. The share of population living on less than $3.00 a day is the first indicator the World Bank tracks in its Bank\u2019s expanded vision indicators to create a world free of poverty in a livable planet. It is also the indicator the UN tracks for SDG 1.1. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.POV.DDAY (https://data.worldbank.org/indicator/SI.POV.DDAY). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.POV.DDAY", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Share of the population living in extreme poverty", "tolerance": 5, "isProjection": false, "shortUnit": "%", "unit": "%" }, "presentation": { "title_public": "Poverty headcount ratio at $2.15 a day (2021 PPP) (% of population)" } }, "si_pov_gaps": { "title": "Poverty gap at $3.00 a day (2021 PPP) (%)", "description_from_producer": "Poverty gap at $3.00 a day (2021 PPP) is the mean shortfall in income or consumption from the poverty line $3.00 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in June 2025, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, 2011, 2021, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the median of the poverty lines found in 23 low-income countries. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.The statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): The poverty gap measures the average shortfall in income or consumption of individuals living below the international poverty line of $3.00 per day, adjusted to 2021 purchasing power parity (PPP), expressed as a percentage of that poverty line. It essentially measures the depth of poverty\u2014that is, how far the extreme poor are living below the poverty line.\n\n### Development relevance:\nThe World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The poverty gap measures the depth of poverty\u2014that is, how far below the poverty line extreme poor are living. The poverty gap measure is used to estimate the total value of monetary transfers that could lift the poor out of poverty, assuming poverty is transitory and there are no administrative costs of transfers.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.POV.GAPS (https://data.worldbank.org/indicator/SI.POV.GAPS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.POV.GAPS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_pov_gini": { "title": "Gini index", "description_from_producer": "Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.\n\n### Statistical concept and methodology:\nMethodology: The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.\n\n\n\n\n\n\n\nThe Gini index provides a convenient summary measure of the degree of inequality. Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.\n\n\n\n\n\n\n\nThe distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.\n\n\n\n\n\n\n\nThe year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.\nStatistical concept(s): The Gini index is the average of all pairwise absolute differences between individual consumption or income, normalized by twice the mean. More intuitively, the Gini index is the average share of mean consumption or income that needs to be transferred between two randomly selected individuals to achieve equality. A Gini index of 1 represents perfect inequality, in which total consumption or income goes to one individual. A Gini index of 0 indicates represents perfect equality, in which all individuals have the same level of consumption or income.\n\n### Development relevance:\nThe World Bank Group's vision of promoting shared prosperity includes a measure that tracks the number of economies with high inequality, defined as those with a Gini index greater than 0.4\n\n### Limitations and exceptions:\nGini coefficients are not unique. It is possible for two different Lorenz curves to give rise to the same Gini coefficient. Furthermore it is possible for the Gini coefficient of a developing country to rise (due to increasing inequality of income) while the number of people in absolute poverty decreases. This is because the Gini coefficient measures relative, not absolute, wealth.\n\n\n\n\n\n\n\nAnother limitation of the Gini coefficient is that it is not additive across groups, i.e. the total Gini of a society is not equal to the sum of the Gini's for its sub-groups. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini's, although a Gini coefficient can be computed for the aggregate.\n\n\n\n\n\n\n\nBecause the underlying household surveys differ in methods and types of welfare measures collected, data are not strictly comparable across countries or even across years within a country. Two sources of non-comparability should be noted for distributions of income in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution.\n\n\n\n\n\n\n\nWorld Bank staff have made an effort to ensure that the data are as comparable as possible. Wherever possible, consumption has been used rather than income. Income distribution and Gini indexes for high-income economies are calculated directly from the Luxembourg Income Study database, using an estimation method consistent with that applied for developing countries.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.POV.GINI (https://data.worldbank.org/indicator/SI.POV.GINI). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.POV.GINI", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_pov_lmic": { "title": "Poverty headcount ratio at $4.20 a day (2021 PPP) (% of population)", "description_from_producer": "Poverty headcount ratio at $4.20 a day is the percentage of the population living on less than $4.20 a day at 2021 international prices.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\n\n\n\n\n\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\n\n\n\n\n\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n\n\n\n\n\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): Poverty headcount ratio at $4.20 a day refers to the percentage of a population whose consumption or income per day falls short of $4.20 a day (adjusted for purchasing power parity differences across countries). $4.20 is the typical poverty line of lower-middle-income countries.\n\n### Development relevance:\nThe World Bank defines the poverty line of $4.20 (adjusted for purchasing power differences across countries) as a more relevant poverty line to monitor poverty in lower-middle-income countries. The value of $4.20 is the typical poverty line of lower-middle-income countries, which is the estimated minimum amount of money people in lower-middle-income countries need to cover their daily basic needs, including food, clothing, shelter, health care, education, and so on. Policy dialogue can be facilitated with this poverty line, especially in middle-income countries.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.POV.LMIC (https://data.worldbank.org/indicator/SI.POV.LMIC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.POV.LMIC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_pov_lmic_gp": { "title": "Poverty gap at $4.20 a day (2021 PPP) (%)", "description_from_producer": "Poverty gap at $4.20 a day (2021 PPP) is the mean shortfall in income or consumption from the poverty line $4.20 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.\n\n\n\n\n\n\n\nSince World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.\n\n\n\n\n\n\n\nEarly editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.\n\n\n\n\n\n\n\nThe statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): The poverty gap at $4.20 measures the average shortfall in income or consumption of individuals living below the poverty line of $4.20 per day, adjusted to 2021 purchasing power parity (PPP), expressed as a percentage of that poverty line. It essentially measures the depth of poverty\u2014that is, how far the poor are living below the lower-middle-income poverty line.\n\n### Development relevance:\nMonitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The poverty gap measures the depth of poverty\u2014that is, how far below the lower-middle-income poverty line the poor are living. The poverty gap measure is used to estimate the total value of monetary transfers that could lift the poor out of poverty, assuming poverty is transitory and there are no administrative costs of transfers.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.POV.LMIC.GP (https://data.worldbank.org/indicator/SI.POV.LMIC.GP). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.POV.LMIC.GP", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_pov_mpun": { "title": "Multidimensional poverty headcount ratio (UNDP & OPHI) (% of population)", "description_from_producer": "The multidimensional poverty headcount ratio (UNDP & OPHI) is the percentage of a population living in poverty according to the multidimensional poverty index of the UNDP and OPHI. The index includes three dimensions -- health, education, and living standards.\n\n### Statistical concept and methodology:\nMethodology: The global MPI, published annually since 2010, captures acute multidimensional poverty in developing regions of the world (Alkire and Santos, 2014). This measure is based on the dual-cutoff counting methodology developed by Alkire and Foster (2011). The global MPI is composed of three dimensions (health, education, and living standards) and 10 corresponding indicators (nutrition, child mortality, school attendance, years of schooling, electricity, drinking water, sanitation, cooking fuel, housing, and assets). The global MPI begins by constructing a deprivation profile for each household and person in it that tracks deprivations in each of the 10 indicators. For example, a household and all people living in it are deprived if any child is stunted or any child or adult for whom data are available is underweight; if any child died in the past five years; if any school-aged child is not attending school up to the age at which he or she would complete class 8 or no household member has completed six years of schooling; or if the household lacks access to electricity, an improved source of drinking water within a 30 minute walk round trip, an improved sanitation facility that is not shared, nonsolid cooking fuel, durable housing materials, and basic assets such as a radio, animal cart, phone, television, computer, refrigerator, bicycle or motorcycle. All indicators are equally weighted within each dimension, so the health and education indicators are weighted 1/6 each, and the standard of living indicators are weighted 1/18 each. A person\u2019s deprivation score is the sum of the weighted deprivations she or he experiences. The global MPI identifies people as multidimensionally poor if their deprivation score is 1/3 or higher. MPI values are the product of the incidence (H, or the proportion of population who live in multidimensional poverty) and intensity of poverty (A, or the average deprivation score among multidimensionally poor people). MPI = H \u00d7 A. The MPI ranges from 0 to 1, and higher values imply higher poverty. MPI values decline when fewer people are poor or when poor people have fewer deprivations.\nStatistical concept(s): The multidimensional poverty index (MPI) of the UNDP and OPHI summarizes the incidence of multiple dimensions of poverty in a country. The MPI assesses deprivation in three dimensions of well-being (health, education, and living standards) and 10 corresponding indicators (nutrition, child mortality, school attendance, years of schooling, and access to electricity, safe drinking water, improved sanitation, good cooking fuel, good housing infrastructure).\n\n### Development relevance:\nThe global MPI was developed by Sabina Alkire and Maria Emma Santos (2014), in collaboration with the Human Development Report Office (HDRO) at UNDP as an internationally comparable measure of acute poverty. Technically, the global MPI relies on the Alkire-Foster method (2011). In 2018, five out of the ten indicators have been revised to better align with the SDGs (see Alkire, Kanagaratnam, Nogales and Suppa 2022; Alkire and Kanagaratnam 2020; Alkire and Jahan 2018).\n\n### Limitations and exceptions:\nBesides the frequency and timeliness of survey data, other limitations include if the household survey data being used is missing any of the 10 indicators, that indicator is dropped from the calculation. The weights are then adjusted so that each dimension continues to be given a weight of one-third. MPI poverty estimates are only calculated if at least one indicator in health and education dimensions is available, and if at least four indicators in the living standards dimension are available.", "origins": [ { "producer": "Oxford Poverty and Human Development Initiative (OPHI), University of Oxford, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Alkire, S., Mishra, R., Selden, L. and Suppa, N. (2025). \u2018The global Multidimensional Poverty Index (MPI) 2025: Country results and methodological note\u2019, OPHI MPI Methodological Note 61, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford.url: https://ophi.org.uk/publications/MN-61, uri: https://ophi.org.uk/publications/MN-61, publisher: Oxford Poverty and Human Development Initiative (OPHI). Indicator SI.POV.MPUN (https://data.worldbank.org/indicator/SI.POV.MPUN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.POV.MPUN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "si_pov_mpwb": { "title": "Multidimensional poverty headcount ratio (World Bank) (% of population)", "description_from_producer": "The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions \u2013 monetary poverty, education, and basic infrastructure services \u2013 to capture a more complete picture of poverty.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank's Multidimensional Poverty Measure (MPM) seeks to understand poverty beyond monetary deprivations by including access to education and basic infrastructure along with the monetary headcount ratio at the $3.00 international poverty line.\n\n\n\nThe measure takes inspiration and guidance from other prominent global multidimensional measures, particularly the Multidimensional Poverty Index (MPI) developed by the United Nations Development Programme (UNDP) and Oxford University but differs from them in one important aspect: it includes monetary poverty less than $3.00 per day, the new International Poverty Line at 2021 PPP (Purchasing Power Parity), as one of the dimensions.\n\n\n\nThe MPM is composed of six indicators: consumption or income, educational attainment, educational enrollment, drinking water, sanitation, and electricity. These are mapped into three dimensions of well-being: monetary, education, and basic infrastructure services.\n\n\n\nThe three MPM dimensions are weighted equally, and within each dimension each indicator is also weighted equally. Individuals are considered multidimensionally deprived if they fall short of the threshold in at least one dimension or in a combination of indicators equivalent in weight to a full dimension. In other words, households will be considered poor if they are deprived in indicators whose weight adds up to 1/3 or more. Because the monetary dimension is measured using only one indicator, anyone who is income poor is automatically also poor under the multidimensional poverty measure.\n\n\n\nSummarizing the information on the different deprivations into a single index proves useful in making comparisons across populations and across time. However, any aggregation of indicators into a single index always involves a decision on how each indicator is to be weighted.\nStatistical concept(s): The World Bank\u2019s multi-dimensional poverty measure (MPM) summarizes the incidence of multiple dimensions of poverty in a country. It captures the idea that poverty is multi-faceted, including both monetary and non-monetary aspects. It assesses deprivation in three dimensions of well-being (monetary poverty, education, and basic infrastructure) and 6 corresponding indicators (monetary poverty, educational attainment, educational enrollment, access to electricity, access to improved sanitation, access to safe drinking water).\n\n### Development relevance:\nMonitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The first Sustainable Development Goal calls for ending poverty in all forms by 2030. Poverty is multidimensional, containing both monetary and non-monetary dimensions. The World Bank's multidimensional poverty measure captures both elements, allowing for a more complete tracking of poverty in all forms\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.POV.MPWB (https://data.worldbank.org/indicator/SI.POV.MPWB). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.POV.MPWB", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "si_pov_nahc": { "title": "Poverty headcount ratio at national poverty lines (% of population)", "description_from_producer": "National poverty headcount ratio is the percentage of the population living below the national poverty line(s). National estimates are based on population-weighted subgroup estimates from household surveys. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: Poverty headcount ratio among the population is measured based on national (i.e. country-specific) poverty lines. A country may have a unique national poverty line or separate poverty lines for rural and urban areas, or for different geographic areas to reflect differences in the cost of living or sometimes to reflect differences in diets and consumption baskets.\n\n\n\n\n\n\n\nPoverty estimates at national poverty lines are computed from household survey data collected from nationally representative samples of households. These data must contain sufficiently detailed information to compute a comprehensive estimate of total household income or consumption (including consumption or income from own production), from which it is possible to construct a correctly weighted distribution of per capita consumption or income.\n\n\n\n\n\n\n\nNational poverty lines are the benchmark for estimating poverty indicators that are consistent with the country's specific economic and social circumstances. National poverty lines reflect local perceptions of the level and composition of consumption or income needed to be non-poor. The perceived boundary between poor and non-poor typically rises with the average income of a country and thus does not provide a uniform measure for comparing poverty rates across countries. While poverty rates at national poverty lines should not be used for comparing poverty rates across countries, they are appropriate for guiding and monitoring the results of country-specific national poverty reduction strategies.\n\n\n\n\n\n\n\nAlmost all national poverty lines in developing economies are anchored to the cost of a food bundle - based on the prevailing national diet of the poor - that provides adequate nutrition for good health and normal activity, plus an allowance for nonfood spending. National poverty lines must be adjusted for inflation between survey years to remain constant in real terms and thus allow for meaningful comparisons of poverty over time. Because diets and consumption baskets change over time, countries periodically recalculate the poverty line based on new survey data. In such cases the new poverty lines should be deflated to obtain comparable poverty estimates from earlier years.\n\n\n\n\n\n\n\nThis series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. For economies for which the data are from EU-SILC, the reported year is the income reference year, which is the year before the survey year. For all other economies, the year reported is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which data collection started.\nStatistical concept(s): National poverty headcount ratio refers to the percentage of a population whose consumption or income per day falls short of the national poverty line. National poverty lines vary by country and over time. In low- and middle-income countries, national poverty lines tend to be absolute poverty lines, thus reflecting the estimated minimum amount of money needed to cover basic needs. In high-income countries, national poverty lines tend to be relative poverty lines, thus reflecting the typical amount of money needed for an individual to afford the typical standard of living and without any restraints to participating fully in the societies in which they live. National poverty lines tend to grow with economic growth, especially in high-income or upper-middle-income countries.\n\n### Development relevance:\nThe poverty rate as defined by national poverty lines reflects the share of the population that fails to meet the standard a country thinks is necessary to cover basic needs (typically in low- and middle-income countries) or afford a decent lifestyle (typically in high-income countries). SDG 1.2 aims to reduce by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions, by 2030.\n\n### Other notes:\nThis series only includes estimates that to the best of our knowledge are reasonably comparable over time for a country. Due to differences in estimation methodologies and poverty lines, estimates should not be compared across countries.", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country\u2013specific) poverty lines., World Bank (WB), note: Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country\u2013specific) poverty lines. Indicator SI.POV.NAHC (https://data.worldbank.org/indicator/SI.POV.NAHC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.POV.NAHC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "si_pov_sopo": { "title": "Poverty headcount ratio at societal poverty line (% of population)", "description_from_producer": "The poverty headcount ratio at societal poverty line is the percentage of a population living in poverty according to the World Bank's Societal Poverty Line. The Societal Poverty Line is expressed in purchasing power adjusted 2021 U.S. dollars and defined as max($3.00, $1.30 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $3.00. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries\u2019 median income grows.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: The Societal Poverty Line (SPL) adopted by the World Bank is calculated in 2021 PPP U.S. dollars as follows: SPL = max (US$3.00, US$1.30 + 0.5 median consumption). By this definition, societal poverty represents a combination of extreme poverty, which is fixed in value for everyone, and a relative dimension of well-being that differs in every country depending on the median level of consumption in that country. In countries with low median consumption (less than US$2.00 per person per day), a rise in median consumption does not change the SPL. Indeed, the SPL has the same value as the IPL in all countries with median consumption up to $3.40. However, as countries with median consumption at more than US$3.40 become richer, and the median consumption increases, the value of the SPL also rises. The slope of one-half, the rate at which the SPL is rising as countries become richer, comes from the empirical association observed between national poverty lines and different measures of overall consumption in society. It indicates that, on average, the national poverty lines are increasing at a rate equal to half the median consumption in the countries. The slope of one-half and the intercept of US$1.30 are the values that most closely fit the data provided by the national poverty lines and overall consumption in each country. The SPL and the International Poverty Line (IPL) share the same empirical underpinning. Both are anchored in the distribution of national poverty lines, which represent countries\u2019 own judgements of what poverty means for them. Whereas the IPL focuses narrowly\u2014and deliberately\u2014on the choices of some of the poorest countries, the SPL is built on information from across the whole range of levels of development. In addition to fitting the data well, the slope coefficient of half the median is widely used by many countries and organizations as a measure of relative poverty and inclusion.\nStatistical concept(s): Poverty headcount ratio at societal poverty line measures the share of the population living below the societal poverty line. The societal poverty line combines elements of both absolute and relative poverty. A minimum amount of money is required for subsistence (absolute poverty), while an additional amount of money is required to afford the typical lifestyle expected in the society in which people live (relative poverty). The societal poverty line is expressed as: max (US$3.00, US$1.30 + 0.5 median consumption) in 2021 PPP U.S. dollars. $1.30 represents the standard for absolute poverty, while 0.5 median consumption represents the standard for relative poverty. In very poor countries, typically with median consumption up to $3.40, it makes more sense to consider that poverty is entirely absolute, so that the effective societal poverty line becomes the international poverty line of $3.00.\n\n### Development relevance:\nMonitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The first Sustainable Development Goal calls for ending poverty in all forms by 2030. Typically, countries' poverty lines increase in real value as their economies get richer. Essentially, this is because in a richer country it is costlier to participate in society (i.e., be considered non-poor). Yet as relative poverty lines can take on very low values for poor countries, one may want to ensure a lower bound which provides a fixed, absolute element to the SPL, which the study interprets as the cost of consuming some minimum bundle of goods. The Societal Poverty Line tracks poverty rates consistent with these considerations.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.POV.SOPO (https://data.worldbank.org/indicator/SI.POV.SOPO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.POV.SOPO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "si_pov_umic": { "title": "Poverty headcount ratio at $8.30 a day (2021 PPP) (% of population)", "description_from_producer": "Poverty headcount ratio at $8.30 a day is the percentage of the population living on less than $8.30 a day at 2021 international prices.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.The statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): Poverty headcount ratio at $8.30 a day refers to the percentage of a population whose consumption or income per day falls short of $8.30 a day (adjusted for purchasing power parity differences across countries). $8.30 is the typical poverty line of upper-middle-income countries.\n\n### Development relevance:\nThe World Bank defines a higher poverty line of $8.30, in addition to the international poverty line of $3.00, to account for higher living standards in a changing world (these values adjust for purchasing power differences across countries). The value of $8.30 is the typical poverty line of upper-middle-income countries, which is the estimated minimum amount of money people in upper-middle-income countries need to cover their daily basic needs, including food, clothing, shelter, heath care, education, and so on. The share of population living on less than $8.30 a day is the second indicator the World Bank tracks in its expanded vision indicators to create a world free of poverty in a livable planet. A growing majority of the world\u2019s population live in middle-income countries (for example, about three-quarters in 2024 compared to one-quarter in 1990), so a higher poverty line would be more representative of the word\u2019s current demographic structure. Further, a higher standard of poverty would be necessary to build resilience in a world more susceptible to climate-related risks.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.POV.UMIC (https://data.worldbank.org/indicator/SI.POV.UMIC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.POV.UMIC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_pov_umic_gp": { "title": "Poverty gap at $8.30 a day (2021 PPP) (%)", "description_from_producer": "Poverty gap at $8.30 a day (2021 PPP) is the mean shortfall in income or consumption from the poverty line $8.30 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $3.00 as the international poverty line using the 2021 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $4.20 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $8.30 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2021 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $3.00 a day in 2021 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.The statistics reported here are based on consumption data or, when unavailable, on income surveys.\nStatistical concept(s): The poverty gap at $8.30 measures the average shortfall in income or consumption of individuals living below the poverty line of $8.30 per day, adjusted to 2021 purchasing power parity (PPP), expressed as a percentage of that poverty line. It essentially measures the depth of poverty\u2014that is, how far the poor are living below the upper-middle-income poverty line.\n\n### Development relevance:\nMonitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The poverty gap measures the depth of poverty\u2014that is, how far below the upper-middle-income poverty line the poor are living. The poverty gap measure is used to estimate the total value of monetary transfers that could lift the poor out of poverty, assuming poverty is transitory and there are no administrative costs of transfers.\n\n### Limitations and exceptions:\nDespite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.\n\n\n\n\n\n\n\nBesides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.\n\n\n\n\n\n\n\nHowever, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.\n\n### Other notes:\nThe World Bank\u2019s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org, note: Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. Indicator SI.POV.UMIC.GP (https://data.worldbank.org/indicator/SI.POV.UMIC.GP). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.POV.UMIC.GP", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_spr_pc40": { "title": "Survey mean consumption or income per capita, bottom 40% of population (2021 PPP $ per day)", "description_from_producer": "Mean consumption or income per capita (2021 PPP $ per day) of the bottom 40%, used in calculating the growth rate in the welfare aggregate of the bottom 40% of the population in the income distribution in a country.\n\n### Statistical concept and methodology:\nMethodology: Survey mean consumption or income per capita, bottom 40% of population measures the amount of consumption or income per person per day in the bottom 40% of the population. It is estimated from survey data and expressed in 2021 PPP dollars.\nStatistical concept(s): Survey mean consumption or income per capita, bottom 40% of population measures the amount of consumption or income per person per day in the bottom 40% of the population. It is estimated from survey data and expressed in 2021 PPP dollars.\n\n### Development relevance:\nIt measures improvements in the well-being of the poor, thus monitoring inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust.\n\n### Limitations and exceptions:\nBecause household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.\n\n### Other notes:\nThe choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity. Indicator SI.SPR.PC40 (https://data.worldbank.org/indicator/SI.SPR.PC40). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.SPR.PC40", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2021 PPP $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "2021 PPP $" } }, "si_spr_pc40_zg": { "title": "Annualized average growth rate in per capita real survey mean consumption or income, bottom 40% of population (%)", "description_from_producer": "The growth rate in the welfare aggregate of the bottom 40% is computed as the annualized average growth rate in per capita real consumption or income of the bottom 40% of the population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2021 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered.\n\n### Statistical concept and methodology:\nMethodology: The annualized growth rate in per capita real survey mean consumption of the bottom 40% is computed in the following steps. First, obtain the mean consumption or income levels of the bottom 40% of the survey distribution in two different periods. The two survey data sets should be comparable - that is, they use a similar method of sampling, collecting data, and constructing the welfare aggregate. Second, the rate of change in the survey mean values of the bottom 40% is annualized.\nStatistical concept(s): The annualized growth rate in per capita real survey mean consumption or income of the bottom 40% measures the rate of change in per capita consumption or income of the bottom 40% in a year.\n\n### Development relevance:\nIt measures improvements in the well-being of the poor, thus monitoring inequality within countries (SDG 10). Inequality undermines economic growth and development in the long run, limits the potential of growth to reduce poverty, and weakens social cohesion and trust.\n\n### Limitations and exceptions:\nBecause household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.\n\n### Other notes:\nThe comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity. Indicator SI.SPR.PC40.ZG (https://data.worldbank.org/indicator/SI.SPR.PC40.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.SPR.PC40.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_spr_pcap": { "title": "Survey mean consumption or income per capita, total population (2021 PPP $ per day)", "description_from_producer": "Mean consumption or income per capita (2021 PPP $ per day) used in calculating the growth rate in the welfare aggregate of total population.\n\n### Statistical concept and methodology:\nMethodology: The choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes.\nStatistical concept(s): Survey mean consumption or income per capita, total population measures the amount of consumption or income per person per day in the population. It is estimated from survey data and expressed in 2021 PPP dollars.\n\n### Development relevance:\nThe World Bank Group has a goal of promoting shared prosperity within and across countries. Growth is good for the poor and growth in poor countries reflects in improvements in the World Bank\u2019s new shared prosperity measure, the Global Prosperity Gap.\n\n### Limitations and exceptions:\nBecause household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.\n\n### Other notes:\nThe choice of consumption or income for a country is made according to which welfare aggregate is used to estimate extreme poverty in the Poverty and Inequality Platform (PIP). The practice adopted by the World Bank for estimating global and regional poverty is, in principle, to use per capita consumption expenditure as the welfare measure wherever available; and to use income as the welfare measure for countries for which consumption is unavailable. However, in some cases data on consumption may be available but are outdated or not shared with the World Bank for recent survey years. In these cases, if data on income are available, income is used. Whether data are for consumption or income per capita is noted in the footnotes. Because household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity. Indicator SI.SPR.PCAP (https://data.worldbank.org/indicator/SI.SPR.PCAP). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.SPR.PCAP", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2021 PPP $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "2021 PPP $" } }, "si_spr_pcap_zg": { "title": "Annualized average growth rate in per capita real survey mean consumption or income, total population (%)", "description_from_producer": "The growth rate in the welfare aggregate of the total population is computed as the annualized average growth rate in per capita real consumption or income of the total population in the income distribution in a country from household surveys over a roughly 5-year period. Mean per capita real consumption or income is measured at 2021 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries means are not reported due to grouped and/or confidential data. The annualized growth rate is computed as (Mean in final year/Mean in initial year)^(1/(Final year - Initial year)) - 1. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported. The initial year refers to the nearest survey collected 5 years before the most recent survey available, only surveys collected between 3 and 7 years before the most recent survey are considered.\n\n### Statistical concept and methodology:\nMethodology: The annualized growth rate in per capita real survey mean consumption of the total population is computed in the following steps. First, obtain the mean consumption or income levels of the total population of the survey distribution in two different periods. The two survey data sets should be comparable - that is, they use a similar method of sampling, collecting data, and constructing the welfare aggregate. Second, the rate of change in the survey mean values of the total population is annualized.\nStatistical concept(s): The annualized growth rate in per capita real survey mean consumption or income measures the rate of change in per capita consumption or income in a year.\n\n### Development relevance:\nThe World Bank Group has a goal of promoting shared prosperity within and across countries. Growth is good for the poor and growth in poor countries reflects in improvements in the World Bank\u2019s new shared prosperity measure, the Global Prosperity Gap.\n\n### Limitations and exceptions:\nBecause household surveys are infrequent in most countries and are not aligned across countries, comparisons across countries or over time should be made with a high degree of caution.\n\n### Other notes:\nThe comparability of welfare aggregates (consumption or income) for the chosen years T0 and T1 is assessed for every country. If comparability across the two surveys is a major concern for a country, the selection criteria are re-applied to select the next best survey year(s). Annualized growth rates are calculated between the survey years, using a compound growth formula. The survey years defining the period for which growth rates are calculated and the type of welfare aggregate used to calculate the growth rates are noted in the footnotes.", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity. Indicator SI.SPR.PCAP.ZG (https://data.worldbank.org/indicator/SI.SPR.PCAP.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.SPR.PCAP.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "si_spr_pgap": { "title": "Prosperity gap (average shortfall from a prosperity standard of $28/day)", "description_from_producer": "The average shortfall from a prosperity standard of $28 per day (adjusted for differences in purchasing power parity across countries). It is measured as the average factor by which incomes fall short of $28.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: The Prosperity Gap is a measure of shared prosperity. As a distribution-sensitive measure, the gap narrows when incomes increase anywhere and falls fastest when incomes of the very poorest increase: growth in income of a person earning $3.00 per day gets ten times more weight than growth in income of a person earning $28/day. Improvements (i.e., reductions) in the Prosperity Gap reflect increases in average income, reductions in inequality within countries, and (for global/regional aggregates) reductions in inequality between countries. It is estimated from nationally representative household surveys. When survey data are missing, in order to create global and regional aggregates, data are interpolated and extrapolated following the same methods that are used for global poverty estimates: https://datanalytics.worldbank.org/PIP-Methodology/lineupestimates.html\n\n\nStatistical concept(s): The Prosperity Gap measures the average factor by which everyone\u2019s incomes in a society needs to vary to reach a prosperity standard of $28 per day (expressed in 2021 PPP dollars). Consider the following example of five individuals earning $2, $7, $14, $28, and $56. Their incomes will have to vary by a factor of 14, 4, 2, 1, and 0.5, respectively, to achieve a prosperity standard of $28. If the world consisted of only these five individuals, the Global Prosperity Gap would be the average of these factors (4.3). As shown in this example, poorer individuals contribute more to the Global Prosperity Gap. $28 is roughly the per capita household income at which countries transition from upper-middle-income to high-income status.\n\n### Development relevance:\nThe World Bank Group's mission is to end extreme poverty and boost shared prosperity on a livable planet. Boosting shared prosperity is key to ensure that development gains are shared across vulnerable groups. The goal of boosting shared prosperity is defined using the prosperity gap.", "origins": [ { "producer": "World Bank Poverty and Inequality Platform", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org., World Bank (WB), uri: http://pip.worldbank.org. Indicator SI.SPR.PGAP (https://data.worldbank.org/indicator/SI.SPR.PGAP). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.SPR.PGAP", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sl_agr_0714_fe_zs": { "title": "Child employment in agriculture, female (% of female economically active children ages 7-14)", "description_from_producer": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Agriculture corresponds to division 1 (ISIC revision 2), categories A and B (ISIC revision 3), or category A (ISIC revision 4) and includes hunting, forestry, and fishing. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.AGR.0714.FE.ZS (https://data.worldbank.org/indicator/SL.AGR.0714.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.AGR.0714.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female economically active children ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female economically active children ages 7-14" } }, "sl_agr_0714_ma_zs": { "title": "Child employment in agriculture, male (% of male economically active children ages 7-14)", "description_from_producer": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Agriculture corresponds to division 1 (ISIC revision 2), categories A and B (ISIC revision 3), or category A (ISIC revision 4) and includes hunting, forestry, and fishing. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.AGR.0714.MA.ZS (https://data.worldbank.org/indicator/SL.AGR.0714.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.AGR.0714.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male economically active children ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male economically active children ages 7-14" } }, "sl_agr_0714_zs": { "title": "Child employment in agriculture (% of economically active children ages 7-14)", "description_from_producer": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Agriculture corresponds to division 1 (ISIC revision 2), categories A and B (ISIC revision 3), or category A (ISIC revision 4) and includes hunting, forestry, and fishing. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.AGR.0714.ZS (https://data.worldbank.org/indicator/SL.AGR.0714.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.AGR.0714.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of economically active children ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of economically active children ages 7-14" } }, "sl_agr_empl_fe_zs": { "title": "Employment in agriculture, female (% of female employment) (modeled ILO estimate)", "description_short": "Share of total employment working in the agriculture sector.", "description_from_producer": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\n### Development relevance:\nSectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries.\n\n### Limitations and exceptions:\nThere are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata.", "description_key": "- Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment.\n- The agriculture sector includes crop and animal production, hunting, forestry, logging, and fishing and aquaculture activities, as defined by the [International Standard Industrial Classification of All Economic Activities (ISIC)](https://ilostat.ilo.org/methods/concepts-and-definitions/classification-economic-activities/).\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.AGR.EMPL.FE.ZS (https://data.worldbank.org/indicator/SL.AGR.EMPL.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.AGR.EMPL.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Female employment in agriculture", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_agr_empl_ma_zs": { "title": "Employment in agriculture, male (% of male employment) (modeled ILO estimate)", "description_short": "Share of total employment working in the agriculture sector.", "description_from_producer": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\n### Development relevance:\nSectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries.\n\n### Limitations and exceptions:\nThere are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata.", "description_key": "- Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment.\n- The agriculture sector includes crop and animal production, hunting, forestry, logging, and fishing and aquaculture activities, as defined by the [International Standard Industrial Classification of All Economic Activities (ISIC)](https://ilostat.ilo.org/methods/concepts-and-definitions/classification-economic-activities/).\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.AGR.EMPL.MA.ZS (https://data.worldbank.org/indicator/SL.AGR.EMPL.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.AGR.EMPL.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Male employment in agriculture", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_agr_empl_zs": { "title": "Employment in agriculture (% of total employment) (modeled ILO estimate)", "description_short": "Share of total employment working in the agriculture sector.", "description_from_producer": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\n### Development relevance:\nSectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries.\n\n### Limitations and exceptions:\nThere are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectorsdata.", "description_key": "- Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment.\n- The agriculture sector includes crop and animal production, hunting, forestry, logging, and fishing and aquaculture activities, as defined by the [International Standard Industrial Classification of All Economic Activities (ISIC)](https://ilostat.ilo.org/methods/concepts-and-definitions/classification-economic-activities/).\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.AGR.EMPL.ZS (https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.AGR.EMPL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Employment in agriculture", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_emp_1524_sp_fe_ne_zs": { "title": "Employment to population ratio, ages 15-24, female (%) (national estimate)", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.EMP.1524.SP.FE.NE.ZS (https://data.worldbank.org/indicator/SL.EMP.1524.SP.FE.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.1524.SP.FE.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_emp_1524_sp_fe_zs": { "title": "Employment to population ratio, ages 15-24, female (%) (modeled ILO estimate)", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.1524.SP.FE.ZS (https://data.worldbank.org/indicator/SL.EMP.1524.SP.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.1524.SP.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_1524_sp_ma_ne_zs": { "title": "Employment to population ratio, ages 15-24, male (%) (national estimate)", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.EMP.1524.SP.MA.NE.ZS (https://data.worldbank.org/indicator/SL.EMP.1524.SP.MA.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.1524.SP.MA.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_emp_1524_sp_ma_zs": { "title": "Employment to population ratio, ages 15-24, male (%) (modeled ILO estimate)", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.1524.SP.MA.ZS (https://data.worldbank.org/indicator/SL.EMP.1524.SP.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.1524.SP.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_1524_sp_ne_zs": { "title": "Employment to population ratio, ages 15-24, total (%) (national estimate)", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.EMP.1524.SP.NE.ZS (https://data.worldbank.org/indicator/SL.EMP.1524.SP.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.1524.SP.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_emp_1524_sp_zs": { "title": "Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.1524.SP.ZS (https://data.worldbank.org/indicator/SL.EMP.1524.SP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.1524.SP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_mpyr_fe_zs": { "title": "Employers, female (% of female employment) (modeled ILO estimate)", "description_from_producer": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.MPYR.FE.ZS (https://data.worldbank.org/indicator/SL.EMP.MPYR.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.MPYR.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_mpyr_ma_zs": { "title": "Employers, male (% of male employment) (modeled ILO estimate)", "description_from_producer": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.MPYR.MA.ZS (https://data.worldbank.org/indicator/SL.EMP.MPYR.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.MPYR.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_mpyr_zs": { "title": "Employers, total (% of total employment) (modeled ILO estimate)", "description_from_producer": "Employers are those workers who, working on their own account or with one or a few partners, hold the type of jobs defined as a \"self-employment jobs\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of employers to the total employed is calculated as follows: Employers/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.MPYR.ZS (https://data.worldbank.org/indicator/SL.EMP.MPYR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.MPYR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_self_fe_zs": { "title": "Self-employed, female (% of female employment) (modeled ILO estimate)", "description_from_producer": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.SELF.FE.ZS (https://data.worldbank.org/indicator/SL.EMP.SELF.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.SELF.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_self_ma_zs": { "title": "Self-employed, male (% of male employment) (modeled ILO estimate)", "description_from_producer": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.SELF.MA.ZS (https://data.worldbank.org/indicator/SL.EMP.SELF.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.SELF.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_self_zs": { "title": "Self-employed, total (% of total employment) (modeled ILO estimate)", "description_from_producer": "Self-employed workers are those workers who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a \"self-employment jobs.\" i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include sub-categories of employers, own-account workers and members of producers' cooperatives and contributing family workers.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of self-employed workers to the total employed is calculated as follows: Self-employed workers/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.SELF.ZS (https://data.worldbank.org/indicator/SL.EMP.SELF.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.SELF.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_smgt_fe_zs": { "title": "Female share of employment in senior and middle management (%)", "description_from_producer": "The female share of employment in senior and middle management conveys the number of women in management as a percentage of employment in management. Employment in management is defined based on the International Standard Classification of Occupations. This series refers to senior and middle management only, thus excluding junior management (category 1 in both ISCO-08 and ISCO-88 minus category 14 in ISCO-08 and minus category 13 in ISCO-88).\n\n### Statistical concept and methodology:\nMethodology: The data for this indicator is collected through labor force surveys or any other household survey which collects such data through a module on employment. Establishment/firm surveys or administrative records can also provide useful data on female-occupied management positions by ISCO groups. Surveys are conducted by national statistical offices or ministries of labor in countries.\nStatistical concept(s): Employment comprises all persons of working age who, during a short reference period (one week), were engaged in any activity to produce goods or provide services for pay or profit. For further clarification, see: Resolution concerning statistics of work, employment and labor underutilization (2013).\n\n\n\n\n\n\n\nEmployment in management is determined according to the categories of the latest version of the International Standard Classification of Occupations (ISCO-08), which organizes jobs into a clearly defined set of groups based on the tasks and duties undertaken in the job. For the purposes of this indicator, it is preferable to refer separately to senior and middle management only on one hand, and to total management (including junior management) on the other. Senior and middle management correspond to sub-major groups 11, 12 and 13 in ISCO-08 and sub-major groups 11 and 12 in ISCO-88. If statistics are not available disaggregated at the sub-major group level (two-digit level of ISCO), then major group 1 of ISCO-88 and ISCO-08 can be used as a proxy and the indicator would then refer only to total management (including junior management).\n\n### Development relevance:\nThis indicator provides a meaningful measure of the percentage of females who are employed in decision-making and management roles in government, large enterprises and institutions, thus providing some insight into women\u2019s power in decision-making and in the economy, relative to men\u2019s power.\n\n### Limitations and exceptions:\nThe main limitation of this indicator is that it fails to capture the differences in the levels of responsibility of women in their respective managerial position, or the importance of the enterprises and organizations in which they are employed. Its quality is also significantly impacted by the reliability of the employment statistics by occupation at the two-digit level of the ISCO. Whenever data at the two-digit level of the ISCO are not available, data at the one-digit level could be used as a proxy, referring only to major group 1 of ISCO-08 or ISCO-88, rather than to also refer to major group 1 minus category 14 of ISCO-08 or major group 1 minus category 13 of ISCO-88. This implies referring to the female share in total management, rather than also to the female share in senior and middle management exclusively. This proxy should be used only in case of lack of availability of data at the two-digit level of the ISCO, as total management includes junior management, and women tend to be more represented in junior management positions than in senior or middle management positions, and thus, by referring only to total management one may over-estimate women\u2019s impact in high-level decision-making roles.", "origins": [ { "producer": "Labour Market-related SDG Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Market-related SDG Indicators database (ILOSDG), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.EMP.SMGT.FE.ZS (https://data.worldbank.org/indicator/SL.EMP.SMGT.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.SMGT.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sl_emp_totl_sp_fe_ne_zs": { "title": "Employment to population ratio, 15+, female (%) (national estimate)", "description_short": "Share of the female working-age population who are employed. Figures refer to [national estimates](#dod:ilo-national-estimates) and may not be fully comparable across countries.", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- This indicator is defined as the proportion of a country\u2019s working-age population that is employed. It is also known as the employment-to-population ratio. Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment. This indicator gives a broad sense of how many people in a country are working, regardless of the kind of job they have or how many hours they work.\n- When defining the labor force, the working-age population includes all people above the legal working age for employment in each country. The ILO\u2019s guidelines define it as people aged 15 and older, but national definitions vary, depending on local laws. Some countries also set an upper age limit.\n- This data comes from national sources, typically labor force surveys, household surveys, or population censuses. Each country uses its own definitions and methods, so this data may not be directly comparable across countries and over time.\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.EMP.TOTL.SP.FE.NE.ZS (https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.FE.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.FE.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Female employment rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Female employment rate", "note": "Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "National estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_emp_totl_sp_fe_zs": { "title": "Employment to population ratio, 15+, female (%) (modeled ILO estimate)", "description_short": "Share of the female working-age population (ages 15 and older) who are employed.", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- This indicator is defined as the proportion of a country\u2019s working-age population that is employed. It is also known as the employment-to-population ratio. Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment. This indicator gives a broad sense of how many people in a country are working, regardless of the kind of job they have or how many hours they work.\n- When defining the working-age population, the definition of \u201cworking age\u201d varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.TOTL.SP.FE.ZS (https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Female employment rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Female employment rate", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_emp_totl_sp_ma_ne_zs": { "title": "Employment to population ratio, 15+, male (%) (national estimate)", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.EMP.TOTL.SP.MA.NE.ZS (https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.MA.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.MA.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_emp_totl_sp_ma_zs": { "title": "Employment to population ratio, 15+, male (%) (modeled ILO estimate)", "description_short": "Share of the male working-age population (ages 15 and older) who are employed.", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- This indicator is defined as the proportion of a country\u2019s working-age population that is employed. It is also known as the employment-to-population ratio. Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment. This indicator gives a broad sense of how many people in a country are working, regardless of the kind of job they have or how many hours they work.\n- When defining the working-age population, the definition of \u201cworking age\u201d varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.TOTL.SP.MA.ZS (https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Male employment rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Male employment rate", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_emp_totl_sp_ne_zs": { "title": "Employment to population ratio, 15+, total (%) (national estimate)", "description_short": "Share of the working-age population who are employed. Figures refer to [national estimates](#dod:ilo-national-estimates) and may not be fully comparable across countries.", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\n\n\nEPR (%) = 100 x Persons employed / Working-age population\n\n\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\n\n\nEPRw (%) = 100 x Employed women / Working-age women\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\n\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nComparability of employment-to-population ratios across countries is affected most significantly by variations in the definitions used for the employment and population figures. Differences result from age coverage, such as the lower and upper bounds for labor force activity. Estimates of both employment and population are also likely to vary according to whether members of the armed forces are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labor force survey have to do with national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure \u201call persons engaged\u201d, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment-to-population ratios can vary for this reason alone. Countries with employment-to-population ratios based on less than full-year survey periods can be expected to have ratios that are not directly comparable with those from full-year, month-by-month collections. For example, an annual average based on 12 months of observations, all other things being equal, is likely to be different from an annual average based on four (quarterly) observations.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- This indicator is defined as the proportion of a country\u2019s working-age population that is employed. It is also known as the employment-to-population ratio. Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment. This indicator gives a broad sense of how many people in a country are working, regardless of the kind of job they have or how many hours they work.\n- When defining the labor force, the working-age population includes all people above the legal working age for employment in each country. The ILO\u2019s guidelines define it as people aged 15 and older, but national definitions vary, depending on local laws. Some countries also set an upper age limit.\n- This data comes from national sources, typically labor force surveys, household surveys, or population censuses. Each country uses its own definitions and methods, so this data may not be directly comparable across countries and over time.\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.EMP.TOTL.SP.NE.ZS (https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Employment rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Employment rate", "note": "Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "National estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_emp_totl_sp_zs": { "title": "Employment to population ratio, 15+, total (%) (modeled ILO estimate)", "description_short": "Share of the working-age population (ages 15 and older) who are employed.", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- This indicator is defined as the proportion of a country\u2019s working-age population that is employed. It is also known as the employment-to-population ratio. Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment. This indicator gives a broad sense of how many people in a country are working, regardless of the kind of job they have or how many hours they work.\n- When defining the working-age population, the definition of \u201cworking age\u201d varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.TOTL.SP.ZS (https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Employment rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Employment rate", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_emp_vuln_fe_zs": { "title": "Vulnerable employment, female (% of female employment) (modeled ILO estimate)", "description_from_producer": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "International Labour Organization (ILOEST/ILOSTAT) and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB). Indicator SL.EMP.VULN.FE.ZS (https://data.worldbank.org/indicator/SL.EMP.VULN.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.VULN.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_vuln_ma_zs": { "title": "Vulnerable employment, male (% of male employment) (modeled ILO estimate)", "description_from_producer": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "International Labour Organization (ILOEST/ILOSTAT) and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB). Indicator SL.EMP.VULN.MA.ZS (https://data.worldbank.org/indicator/SL.EMP.VULN.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.VULN.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_vuln_zs": { "title": "Vulnerable employment, total (% of total employment) (modeled ILO estimate)", "description_short": "Share of employed population who are working as independent own-account workers (self-employed without staff), or contributing family workers (self-employed in a household business or farm).", "description_from_producer": "Vulnerable employment is contributing family workers and own-account workers as a percentage of total employment.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of vulnerable employment to the total employed is calculated as follows: (Contributing family workers + own-account workers)/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks. The vulnerable employment rate, which is the share of vulnerable employment in total employment, was an indicator of the (now finished) Millennium Development Goals, under the employment, target on decent work.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "description_key": "- This indicator shows the share of employed people in vulnerable employment \u2013 those working as independent own-account workers or as contributing family members. These groups are more likely to lack formal arrangements and social protection, and to have variable income.\n- Independent own-account workers are defined as workers in self-employment jobs who do not engage any employees to work with them. Contributing family members are workers in self-employment jobs who work in an enterprise operated by a related person living in the same household. These groups are defined according to the [International Standard Classification of Status in Employment (ICSE-93)](#dod:icse-93).\n- A higher share indicates greater labor market vulnerability, which is more common in lower-income countries. Over time, the share tends to fall as countries get richer and more workers move into wage and salaried employment. It\u2019s important to note that this is a broad measure: even within this category, people\u2019s work circumstances can vary widely. For example, some independent own-account workers in rich countries are highly paid professionals or freelancers, and would not usually be considered \u201cvulnerable\u201d.\n- When defining the working-age population, the definition of \u201cworking age\u201d varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "International Labour Organization (ILOEST/ILOSTAT) and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB). Indicator SL.EMP.VULN.ZS (https://data.worldbank.org/indicator/SL.EMP.VULN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.VULN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Share in vulnerable employment", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Share of workers in vulnerable employment", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Types of jobs are classified by status according to the [ICSE-93 standard](#dod:icse-93). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_emp_work_fe_zs": { "title": "Wage and salaried workers, female (% of female employment) (modeled ILO estimate)", "description_from_producer": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100.\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\n\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.WORK.FE.ZS (https://data.worldbank.org/indicator/SL.EMP.WORK.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.WORK.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_work_ma_zs": { "title": "Wage and salaried workers, male (% of male employment) (modeled ILO estimate)", "description_from_producer": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.WORK.MA.ZS (https://data.worldbank.org/indicator/SL.EMP.WORK.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.WORK.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_emp_work_zs": { "title": "Wage and salaried workers, total (% of total employment) (modeled ILO estimate)", "description_from_producer": "Wage and salaried workers (employees) are those workers who hold the type of jobs defined as \"paid employment jobs,\" where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of wage and salaried workers to the total employed is calculated as follows: Wage and salaried workers /Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.WORK.ZS (https://data.worldbank.org/indicator/SL.EMP.WORK.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.WORK.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_fam_0714_fe_zs": { "title": "Children in employment, unpaid family workers, female (% of female children in employment, ages 7-14)", "description_from_producer": "Unpaid family workers are people who work without pay in a market-oriented establishment operated by a related person living in the same household.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.FAM.0714.FE.ZS (https://data.worldbank.org/indicator/SL.FAM.0714.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.FAM.0714.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female children in employment, ages 7-14" } }, "sl_fam_0714_ma_zs": { "title": "Children in employment, unpaid family workers, male (% of male children in employment, ages 7-14)", "description_from_producer": "Unpaid family workers are people who work without pay in a market-oriented establishment operated by a related person living in the same household.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.FAM.0714.MA.ZS (https://data.worldbank.org/indicator/SL.FAM.0714.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.FAM.0714.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male children in employment, ages 7-14" } }, "sl_fam_0714_zs": { "title": "Children in employment, unpaid family workers (% of children in employment, ages 7-14)", "description_from_producer": "Unpaid family workers are people who work without pay in a market-oriented establishment operated by a related person living in the same household.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.FAM.0714.ZS (https://data.worldbank.org/indicator/SL.FAM.0714.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.FAM.0714.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children in employment, ages 7-14" } }, "sl_fam_work_fe_zs": { "title": "Contributing family workers, female (% of female employment) (modeled ILO estimate)", "description_from_producer": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.FAM.WORK.FE.ZS (https://data.worldbank.org/indicator/SL.FAM.WORK.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.FAM.WORK.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_fam_work_ma_zs": { "title": "Contributing family workers, male (% of male employment) (modeled ILO estimate)", "description_from_producer": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.FAM.WORK.MA.ZS (https://data.worldbank.org/indicator/SL.FAM.WORK.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.FAM.WORK.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_fam_work_zs": { "title": "Contributing family workers, total (% of total employment) (modeled ILO estimate)", "description_from_producer": "Contributing family workers are those workers who hold \"self-employment jobs\" as own-account workers in a market-oriented establishment operated by a related person living in the same household.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The proportion of contributing family workers to the total employed is calculated as follows: Contributing family workers/Total employment x 100.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The status in employment distinguishes between two categories of the total employed: (a) wage and salaried workers (also known as employees); and (b) self-employed workers, with the subcategories: (i) self-employed workers with employees (employers), (ii) self-employed workers without employees (own-account workers), and (iii) members of producers' cooperatives and contributing family workers (also known as unpaid family workers). Vulnerable employment refers to the sum of (ii) own-account workers and (iii) contributing family workers.\n\n### Development relevance:\nBreaking down employment information by status in employment provides a statistical basis for describing workers' behaviour and conditions of work, and for defining an individual's socio-economic group. A high proportion of wage and salaried workers in a country can signify advanced economic development. If the proportion of own-account workers (self-employed without hired employees) is sizeable, it may be an indication of a large agriculture sector and low growth in the formal economy. A high proportion of contributing family workers \u2014 generally unpaid, although compensation might come indirectly in the form of family income \u2014 may indicate weak development, little job growth, and often a large rural economy.\n\n\n\nEach status group faces different economic risks, and contributing family workers and own-account workers are the most vulnerable - and therefore the most likely to fall into poverty. They are the least likely to have formal work arrangements, are the least likely to have social protection and safety nets to guard against economic shocks, and often are incapable of generating sufficient savings to offset these shocks.\n\n### Limitations and exceptions:\nData are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. Due to differences in definitions and coverage across countries, there are limitations for comparing data across countries and over time even within a country. Estimates of women in employment are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.FAM.WORK.ZS (https://data.worldbank.org/indicator/SL.FAM.WORK.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.FAM.WORK.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_gdp_pcap_em_kd": { "title": "GDP per person employed (constant 2021 PPP $)", "description_from_producer": "GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 2021 constant international dollars using PPP rates. An international dollar has the same purchasing power over GDP that a U.S. dollar has in the United States.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank. The employment rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): GDP per person employed represents labor productivity\u2014output per unit of labor input. To compare labor productivity levels across countries, GDP is converted to international dollars using purchasing power parity rates which take account of differences in relative prices between countries.\n\n### Development relevance:\nLabor productivity is used to assess a country's economic ability to create and sustain decent employment opportunities with fair and equitable remuneration. Productivity increases obtained through investment, trade, technological progress, or changes in work organization can increase social protection and reduce poverty, which in turn reduce vulnerable employment and working poverty. Productivity increases do not guarantee these improvements, but without them - and the economic growth they bring - improvements are highly unlikely.\n\n\n\nGDP per person employed is a key measure to monitor whether a country is on track to achieve the Sustainable Development Goal of promoting sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all. [SDG Indicator 8.2.1]\n\n### Limitations and exceptions:\nFor comparability of individual sectors labor productivity is estimated according to national accounts conventions. However, there are still significant limitations on the availability of reliable data. Information on consistent series of output in both national currencies and purchasing power parity dollars is not easily available, especially in developing countries, because the definition, coverage, and methodology are not always consistent across countries. For example, countries employ different methodologies for estimating the missing values for the nonmarket service sectors and use different definitions of the informal sector.", "origins": [ { "producer": "World Bank, International Labour Organization (ILO), UN Population Division, Eurostat, and OECD", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB), note: Estimates are based on employment, population, GDP, and PPP data obtained from International Labour Organization, United Nations Population Division, Eurostat, OECD, and World Bank., type: estimates based on external database;\nInternational Labour Organization (ILO);\nUnited Nations (UN), publisher: UN Population Division;\nEurostat (ESTAT);\nOrganisation for Economic Co-operation and Development (OECD);\nWorld Development Indicators database, World Bank (WB). Indicator SL.GDP.PCAP.EM.KD (https://data.worldbank.org/indicator/SL.GDP.PCAP.EM.KD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.GDP.PCAP.EM.KD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "constant 2021 PPP $", "short_unit": "$", "display": { "numDecimalPlaces": 0, "shortUnit": "$", "unit": "constant 2021 PPP $" } }, "sl_ind_empl_fe_zs": { "title": "Employment in industry, female (% of female employment) (modeled ILO estimate)", "description_short": "Share of total employment working in the industry sector.", "description_from_producer": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\n### Development relevance:\nSectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries.\n\n### Limitations and exceptions:\nThere are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data.", "description_key": "- Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment.\n- The industry sector includes mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), as defined by the [International Standard Industrial Classification of All Economic Activities (ISIC)](https://ilostat.ilo.org/methods/concepts-and-definitions/classification-economic-activities/).\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.IND.EMPL.FE.ZS (https://data.worldbank.org/indicator/SL.IND.EMPL.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.IND.EMPL.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Female employment in industry", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_ind_empl_ma_zs": { "title": "Employment in industry, male (% of male employment) (modeled ILO estimate)", "description_short": "Share of total employment working in the industry sector.", "description_from_producer": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\n### Development relevance:\nSectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries.\n\n### Limitations and exceptions:\nThere are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data.", "description_key": "- Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment.\n- The industry sector includes mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), as defined by the [International Standard Industrial Classification of All Economic Activities (ISIC)](https://ilostat.ilo.org/methods/concepts-and-definitions/classification-economic-activities/).\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.IND.EMPL.MA.ZS (https://data.worldbank.org/indicator/SL.IND.EMPL.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.IND.EMPL.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Male employment in industry", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_ind_empl_zs": { "title": "Employment in industry (% of total employment) (modeled ILO estimate)", "description_short": "Share of total employment working in the industry sector.", "description_from_producer": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\n### Development relevance:\nSectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries.\n\n### Limitations and exceptions:\nThere are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of the three broad sectors data.", "description_key": "- Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment.\n- The industry sector includes mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), as defined by the [International Standard Industrial Classification of All Economic Activities (ISIC)](https://ilostat.ilo.org/methods/concepts-and-definitions/classification-economic-activities/).\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.IND.EMPL.ZS (https://data.worldbank.org/indicator/SL.IND.EMPL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.IND.EMPL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Employment in industry", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_mnf_0714_fe_zs": { "title": "Child employment in manufacturing, female (% of female economically active children ages 7-14)", "description_from_producer": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Manufacturing corresponds to division 3 (ISIC revision 2), category D (ISIC revision 3), or category C (ISIC revision 4). Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.MNF.0714.FE.ZS (https://data.worldbank.org/indicator/SL.MNF.0714.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.MNF.0714.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female economically active children ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female economically active children ages 7-14" } }, "sl_mnf_0714_ma_zs": { "title": "Child employment in manufacturing, male (% of male economically active children ages 7-14)", "description_from_producer": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Manufacturing corresponds to division 3 (ISIC revision 2), category D (ISIC revision 3), or category C (ISIC revision 4). Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.MNF.0714.MA.ZS (https://data.worldbank.org/indicator/SL.MNF.0714.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.MNF.0714.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male economically active children ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male economically active children ages 7-14" } }, "sl_mnf_0714_zs": { "title": "Child employment in manufacturing (% of economically active children ages 7-14)", "description_from_producer": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Manufacturing corresponds to division 3 (ISIC revision 2), category D (ISIC revision 3), or category C (ISIC revision 4). Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.MNF.0714.ZS (https://data.worldbank.org/indicator/SL.MNF.0714.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.MNF.0714.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of economically active children ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of economically active children ages 7-14" } }, "sl_slf_0714_fe_zs": { "title": "Children in employment, self-employed, female (% of female children in employment, ages 7-14)", "description_from_producer": "Self-employed workers are people whose remuneration depends directly on the profits derived from the goods and services they produce, with or without other employees, and include employers, own-account workers, and members of producers cooperatives.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.SLF.0714.FE.ZS (https://data.worldbank.org/indicator/SL.SLF.0714.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.SLF.0714.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female children in employment, ages 7-14" } }, "sl_slf_0714_ma_zs": { "title": "Children in employment, self-employed, male (% of male children in employment, ages 7-14)", "description_from_producer": "Self-employed workers are people whose remuneration depends directly on the profits derived from the goods and services they produce, with or without other employees, and include employers, own-account workers, and members of producers cooperatives.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.SLF.0714.MA.ZS (https://data.worldbank.org/indicator/SL.SLF.0714.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.SLF.0714.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male children in employment, ages 7-14" } }, "sl_slf_0714_zs": { "title": "Children in employment, self-employed (% of children in employment, ages 7-14)", "description_from_producer": "Self-employed workers are people whose remuneration depends directly on the profits derived from the goods and services they produce, with or without other employees, and include employers, own-account workers, and members of producers cooperatives.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.SLF.0714.ZS (https://data.worldbank.org/indicator/SL.SLF.0714.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.SLF.0714.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children in employment, ages 7-14" } }, "sl_srv_0714_fe_zs": { "title": "Child employment in services, female (% of female economically active children ages 7-14)", "description_from_producer": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Services correspond to divisions 6-9 (ISIC revision 2), categories G-P (ISIC revision 3), or categories G-U (ISIC revision 4). Services include wholesale and retail trade, hotels and restaurants, transport, financial intermediation, real estate, public administration, education, health and social work, other community services, and private household activity. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.SRV.0714.FE.ZS (https://data.worldbank.org/indicator/SL.SRV.0714.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.SRV.0714.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female economically active children ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female economically active children ages 7-14" } }, "sl_srv_0714_ma_zs": { "title": "Child employment in services, male (% of male economically active children ages 7-14)", "description_from_producer": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Services correspond to divisions 6-9 (ISIC revision 2), categories G-P (ISIC revision 3), or categories G-U (ISIC revision 4). Services include wholesale and retail trade, hotels and restaurants, transport, financial intermediation, real estate, public administration, education, health and social work, other community services, and private household activity. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.SRV.0714.MA.ZS (https://data.worldbank.org/indicator/SL.SRV.0714.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.SRV.0714.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male economically active children ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male economically active children ages 7-14" } }, "sl_srv_0714_zs": { "title": "Child employment in services (% of economically active children ages 7-14)", "description_from_producer": "Employment by economic activity refers to the distribution of economically active children by the major industrial categories of the International Standard Industrial Classification (ISIC). Services correspond to divisions 6-9 (ISIC revision 2), categories G-P (ISIC revision 3), or categories G-U (ISIC revision 4). Services include wholesale and retail trade, hotels and restaurants, transport, financial intermediation, real estate, public administration, education, health and social work, other community services, and private household activity. Economically active children refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three sectors (Agriculture, Manufacturing and Services) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.SRV.0714.ZS (https://data.worldbank.org/indicator/SL.SRV.0714.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.SRV.0714.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of economically active children ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of economically active children ages 7-14" } }, "sl_srv_empl_fe_zs": { "title": "Employment in services, female (% of female employment) (modeled ILO estimate)", "description_short": "Share of total employment working in the services sector.", "description_from_producer": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\n### Development relevance:\nSectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries.\n\n### Limitations and exceptions:\nThere are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data.", "description_key": "- Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment.\n- The services sector includes wholesale and retail trade; restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business and administrative services; and public administration, community, social, and personal services, as defined by the International Standard Industrial Classification of All Economic Activities (ISIC).\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.SRV.EMPL.FE.ZS (https://data.worldbank.org/indicator/SL.SRV.EMPL.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.SRV.EMPL.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Female employment in services", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_srv_empl_ma_zs": { "title": "Employment in services, male (% of male employment) (modeled ILO estimate)", "description_short": "Share of total employment working in the services sector.", "description_from_producer": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\n### Development relevance:\nSectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries.\n\n### Limitations and exceptions:\nThere are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data.", "description_key": "- Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment.\n- The services sector includes wholesale and retail trade; restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business and administrative services; and public administration, community, social, and personal services, as defined by the International Standard Industrial Classification of All Economic Activities (ISIC).\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.SRV.EMPL.MA.ZS (https://data.worldbank.org/indicator/SL.SRV.EMPL.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.SRV.EMPL.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Male employment in services", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_srv_empl_zs": { "title": "Employment in services (% of total employment) (modeled ILO estimate)", "description_short": "Share of total employment working in the services sector.", "description_from_producer": "Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The International Labour Organization (ILO) classifies economic activity using the International Standard Industrial Classification (ISIC) of All Economic Activities, revision 2 (1968), revision 3 (1990), and revision 4 (2008). Because this classification is based on where work is performed (industry) rather than type of work performed (occupation), all of an enterprise's employees are classified under the same industry, regardless of their trade or occupation. The categories should sum to 100 percent. Where they do not, the differences are due to workers who are not classified by economic activity.\n\n### Development relevance:\nSectoral information is particularly useful in identifying broad shifts in employment and stages of development. In the textbook case of economic development, labor flows from agriculture and other labor-intensive primary activities to industry and finally to the services sector; in the process, workers migrate from rural to urban areas.\n\n\n\nThe breakdown of the indicator by sex allows for analysis of gender segregation of employment by specific sector. Women may be drawn into lower-paying service activities that allow for more flexible work schedules thus making it easier to balance family responsibilities with work life. Segregation of women in certain sectors may also result from cultural attitudes that prevent them from entering industrial employment.\n\n\n\nSegregating one sex in a narrow range of occupations significantly reduces economic efficiency by reducing labor market flexibility and thus the economy's ability to adapt to change. This segregation is particularly harmful for women, who have a much narrower range of labor market choices and lower levels of pay than men. But it is also detrimental to men when job losses are concentrated in industries dominated by men and job growth is centered in service occupations, where women have better chances, as has been the recent experience in many countries.\n\n### Limitations and exceptions:\nThere are many differences in how countries define and measure employment status, particularly members of the armed forces, self-employed workers, and unpaid family workers. Where members of the armed forces are included, they are allocated to the service sector, causing that sector to be somewhat overstated relative to the service sector in economies where they are excluded. Where data are obtained from establishment surveys, data cover only employees; thus self-employed and unpaid family workers are excluded. In such cases the employment share of the agricultural sector is severely underreported. Caution should be also used where the data refer only to urban areas, which record little or no agricultural work. Moreover, the age group and area covered could differ by country or change over time within a country. For detailed information, consult the original source.\n\nCountries also take different approaches to the treatment of unemployed people. In most countries unemployed people with previous job experience are classified according to their last job. But in some countries the unemployed and people seeking their first job are not classifiable by economic activity. Because of these differences, the size and distribution of employment by economic activity may not be fully comparable across countries.\n\nThe ILO reports data by major divisions of the ISIC revision 2, revision 3, or revision 4. Broad classification such as employment by agriculture, industry, and services may obscure fundamental shifts within countries' industrial patterns. A slight majority of countries report economic activity according to the ISIC revision 3 instead of revision 2 or revision 4. The use of one classification or the other should not have a significant impact on the information for the employment of three broad sectors data.", "description_key": "- Employment refers to people who worked for at least an hour during the reference period (typically a week), whether in paid employment or self-employment.\n- The services sector includes wholesale and retail trade; restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business and administrative services; and public administration, community, social, and personal services, as defined by the International Standard Industrial Classification of All Economic Activities (ISIC).\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.SRV.EMPL.ZS (https://data.worldbank.org/indicator/SL.SRV.EMPL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.SRV.EMPL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Employment in services", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_0714_fe_zs": { "title": "Children in employment, female (% of female children ages 7-14)", "description_short": "Share of children aged 7-14 years involved in economic activity for at least one hour in the reference week of the survey.", "description_from_producer": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.FE.ZS (https://data.worldbank.org/indicator/SL.TLF.0714.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Girls in employment", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_0714_ma_zs": { "title": "Children in employment, male (% of male children ages 7-14)", "description_short": "Share of children aged 7-14 years involved in economic activity for at least one hour in the reference week of the survey.", "description_from_producer": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.MA.ZS (https://data.worldbank.org/indicator/SL.TLF.0714.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Boys in employment", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_0714_sw_fe_tm": { "title": "Average working hours of children, study and work, female, ages 7-14 (hours per week)", "description_from_producer": "Average working hours of children studying and working refer to the average weekly working hours of those children who are attending school in combination with economic activity.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.SW.FE.TM (https://data.worldbank.org/indicator/SL.TLF.0714.SW.FE.TM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.SW.FE.TM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "hours per week", "short_unit": "hours", "display": { "shortUnit": "hours", "unit": "hours per week" } }, "sl_tlf_0714_sw_fe_zs": { "title": "Children in employment, study and work, female (% of female children in employment, ages 7-14)", "description_from_producer": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Study and work refer to children attending school in combination with economic activity.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.SW.FE.ZS (https://data.worldbank.org/indicator/SL.TLF.0714.SW.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.SW.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female children in employment, ages 7-14" } }, "sl_tlf_0714_sw_ma_tm": { "title": "Average working hours of children, study and work, male, ages 7-14 (hours per week)", "description_from_producer": "Average working hours of children studying and working refer to the average weekly working hours of those children who are attending school in combination with economic activity.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.SW.MA.TM (https://data.worldbank.org/indicator/SL.TLF.0714.SW.MA.TM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.SW.MA.TM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "hours per week", "short_unit": "hours", "display": { "shortUnit": "hours", "unit": "hours per week" } }, "sl_tlf_0714_sw_ma_zs": { "title": "Children in employment, study and work, male (% of male children in employment, ages 7-14)", "description_from_producer": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Study and work refer to children attending school in combination with economic activity.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.SW.MA.ZS (https://data.worldbank.org/indicator/SL.TLF.0714.SW.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.SW.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male children in employment, ages 7-14" } }, "sl_tlf_0714_sw_tm": { "title": "Average working hours of children, study and work, ages 7-14 (hours per week)", "description_short": "Average weekly hours worked by children aged 7-14 years involved in economic activity and attending school.", "description_from_producer": "Average working hours of children studying and working refer to the average weekly working hours of those children who are attending school in combination with economic activity.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.SW.TM (https://data.worldbank.org/indicator/SL.TLF.0714.SW.TM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.SW.TM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "hours per week", "short_unit": "", "display": { "name": "Average working hours of children, study and work", "numDecimalPlaces": 1, "tolerance": 5, "unit": "hours per week" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_0714_sw_zs": { "title": "Children in employment, study and work (% of children in employment, ages 7-14)", "description_from_producer": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Study and work refer to children attending school in combination with economic activity.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.SW.ZS (https://data.worldbank.org/indicator/SL.TLF.0714.SW.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.SW.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children in employment, ages 7-14" } }, "sl_tlf_0714_wk_fe_tm": { "title": "Average working hours of children, working only, female, ages 7-14 (hours per week)", "description_from_producer": "Average working hours of children working only refers to the average weekly working hours of those children who are involved in economic activity and not attending school.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.WK.FE.TM (https://data.worldbank.org/indicator/SL.TLF.0714.WK.FE.TM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.WK.FE.TM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "hours per week", "short_unit": "hours", "display": { "shortUnit": "hours", "unit": "hours per week" } }, "sl_tlf_0714_wk_fe_zs": { "title": "Children in employment, work only, female (% of female children in employment, ages 7-14)", "description_from_producer": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Work only refers to children involved in economic activity and not attending school.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.WK.FE.ZS (https://data.worldbank.org/indicator/SL.TLF.0714.WK.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.WK.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female children in employment, ages 7-14" } }, "sl_tlf_0714_wk_ma_tm": { "title": "Average working hours of children, working only, male, ages 7-14 (hours per week)", "description_from_producer": "Average working hours of children working only refers to the average weekly working hours of those children who are involved in economic activity and not attending school.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.WK.MA.TM (https://data.worldbank.org/indicator/SL.TLF.0714.WK.MA.TM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.WK.MA.TM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "hours per week", "short_unit": "hours", "display": { "shortUnit": "hours", "unit": "hours per week" } }, "sl_tlf_0714_wk_ma_zs": { "title": "Children in employment, work only, male (% of male children in employment, ages 7-14)", "description_from_producer": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Work only refers to children involved in economic activity and not attending school.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.WK.MA.ZS (https://data.worldbank.org/indicator/SL.TLF.0714.WK.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.WK.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male children in employment, ages 7-14" } }, "sl_tlf_0714_wk_tm": { "title": "Average working hours of children, working only, ages 7-14 (hours per week)", "description_from_producer": "Average working hours of children working only refers to the average weekly working hours of those children who are involved in economic activity and not attending school.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.WK.TM (https://data.worldbank.org/indicator/SL.TLF.0714.WK.TM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.WK.TM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "hours per week", "short_unit": "hours", "display": { "shortUnit": "hours", "unit": "hours per week" } }, "sl_tlf_0714_wk_zs": { "title": "Children in employment, work only (% of children in employment, ages 7-14)", "description_short": "Share of children aged 7-14 years involved in economic activity for at least one hour in the reference week of the survey and not attending school.", "description_from_producer": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Work only refers to children involved in economic activity and not attending school.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.WK.ZS (https://data.worldbank.org/indicator/SL.TLF.0714.WK.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.WK.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Children in employment, work only", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_0714_zs": { "title": "Children in employment, total (% of children ages 7-14)", "description_short": "Share of children aged 7-14 years involved in economic activity for at least one hour in the reference week of the survey.", "description_from_producer": "Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. For detailed source information, see footnotes at each data point.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.TLF.0714.ZS (https://data.worldbank.org/indicator/SL.TLF.0714.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.0714.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Children in employment", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_acti_1524_fe_ne_zs": { "title": "Labor force participation rate for ages 15-24, female (%) (national estimate)", "description_short": "Share of the female working-age population aged 15-24 who are economically active (employed or unemployed). Figures refer to [national estimates](#dod:ilo-national-estimates) and may not be fully comparable across countries.", "description_from_producer": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- This data comes from national sources, typically labor force surveys, household surveys, or population censuses. Each country uses its own definitions and methods, so this data may not be directly comparable across countries and over time.\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.TLF.ACTI.1524.FE.NE.ZS (https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.FE.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.FE.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Female labor force participation rate, ages 15-24", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Youth female labor force participation rate", "note": "Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "National estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_acti_1524_fe_zs": { "title": "Labor force participation rate for ages 15-24, female (%) (modeled ILO estimate)", "description_short": "Share of the female working-age population aged 15-24 who are economically active (employed or unemployed).", "description_from_producer": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.ACTI.1524.FE.ZS (https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Female youth labor force participation rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Female youth labor force participation rate", "subtitle": "Share of the female working-age population aged 15-24 who are economically active (employed or unemployed).", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates", "hasMapTab": true, "tab": "map", "chartTypes": [ "LineChart", "DiscreteBar", "SlopeChart" ], "yAxis": { "min": 0 }, "map": { "colorScale": { "baseColorScheme": "YlGnBu", "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 80, 1 ] } }, "selectedEntityNames": [ "Afghanistan", "Iraq", "Iran", "Pakistan", "India", "Saudi Arabia", "Italy", "Chile", "Spain", "New Zealand", "Mozambique", "United States", "Nigeria" ] }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_acti_1524_ma_ne_zs": { "title": "Labor force participation rate for ages 15-24, male (%) (national estimate)", "description_from_producer": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.TLF.ACTI.1524.MA.NE.ZS (https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.MA.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.MA.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_tlf_acti_1524_ma_zs": { "title": "Labor force participation rate for ages 15-24, male (%) (modeled ILO estimate)", "description_short": "Share of the male working-age population aged 15-24 who are economically active (employed or unemployed).", "description_from_producer": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.ACTI.1524.MA.ZS (https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Male youth labor force participation rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Male youth labor force participation rate", "subtitle": "Share of the male working-age population aged 15-24 who are economically active (employed or unemployed).", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates", "hasMapTab": true, "tab": "map", "chartTypes": [ "LineChart", "DiscreteBar", "SlopeChart" ], "yAxis": { "min": 0 }, "map": { "colorScale": { "baseColorScheme": "YlGnBu", "binningStrategy": "manual", "customNumericValues": [ 20, 30, 40, 50, 60, 70, 80, 1 ] } }, "selectedEntityNames": [ "Afghanistan", "Syria", "United States", "China", "New Zealand", "Peru", "Uganda", "India" ] }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_acti_1524_ne_zs": { "title": "Labor force participation rate for ages 15-24, total (%) (national estimate)", "description_from_producer": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.TLF.ACTI.1524.NE.ZS (https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_tlf_acti_1524_zs": { "title": "Labor force participation rate for ages 15-24, total (%) (modeled ILO estimate)", "description_short": "Share of the working-age population aged 15-24 who are economically active (employed or unemployed).", "description_from_producer": "Labor force participation rate for ages 15-24 is the proportion of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.ACTI.1524.ZS (https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ACTI.1524.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Youth labor force participation rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Youth labor force participation rate", "subtitle": "Share of the working-age population aged 15-24 who are economically active (employed or unemployed).", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates", "hasMapTab": true, "tab": "map", "chartTypes": [ "LineChart", "DiscreteBar", "SlopeChart" ], "yAxis": { "min": 0 }, "map": { "colorScale": { "baseColorScheme": "YlGnBu", "binningStrategy": "manual", "customNumericValues": [ 10, 20, 20, 30, 40, 50, 60, 70, 80, 1 ] } }, "selectedEntityNames": [ "Afghanistan", "Syria", "Uzbekistan", "United States", "Philippines", "China", "New Zealand", "Peru", "Uganda", "India" ] }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_acti_fe_zs": { "title": "Labor force participation rate, female (% of female population ages 15-64) (modeled ILO estimate)", "description_short": "Share of the female working-age population aged 15-64 who are economically active (employed or unemployed).", "description_from_producer": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.ACTI.FE.ZS (https://data.worldbank.org/indicator/SL.TLF.ACTI.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ACTI.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Female labor force participation rate (ages 15-64)", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Female labor force participation rate (ages 15-64)", "subtitle": "Share of the female working-age population aged 15-64 who are economically active (employed or unemployed).", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates", "hasMapTab": true, "tab": "map", "chartTypes": [ "LineChart", "DiscreteBar", "SlopeChart" ], "yAxis": { "min": 0 }, "map": { "colorScale": { "baseColorScheme": "YlGnBu", "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 1 ] } }, "selectedEntityNames": [ "Afghanistan", "Iraq", "Iran", "Pakistan", "India", "Saudi Arabia", "Italy", "Chile", "Spain", "New Zealand", "Mozambique", "United States", "Nigeria" ] }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_acti_ma_zs": { "title": "Labor force participation rate, male (% of male population ages 15-64) (modeled ILO estimate)", "description_short": "Share of the male working-age population aged 15-64 who are economically active (employed or unemployed).", "description_from_producer": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.ACTI.MA.ZS (https://data.worldbank.org/indicator/SL.TLF.ACTI.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ACTI.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Male labor force participation rate (ages 15-64)", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Male labor force participation rate (ages 15-64)", "subtitle": "Share of the male working-age population aged 15-64 who are economically active (employed or unemployed).", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates", "hasMapTab": true, "tab": "map", "chartTypes": [ "LineChart", "DiscreteBar", "SlopeChart" ], "yAxis": { "min": 0 }, "map": { "colorScale": { "baseColorScheme": "YlGnBu", "binningStrategy": "manual", "customNumericValues": [ 40, 50, 60, 70, 80, 90, 100 ] } }, "selectedEntityNames": [ "Afghanistan", "Syria", "United States", "China", "New Zealand", "Peru", "Uganda", "India" ] }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_acti_zs": { "title": "Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)", "description_short": "Share of the working-age population aged 15-64 who are economically active (employed or unemployed).", "description_from_producer": "Labor force participation rate is the proportion of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.ACTI.ZS (https://data.worldbank.org/indicator/SL.TLF.ACTI.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ACTI.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Labor force participation rate (ages 15-64)", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Labor force participation rate (ages 15-64)", "subtitle": "Share of the working-age population aged 15-64 who are economically active (employed or unemployed).", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates", "hasMapTab": true, "tab": "map", "chartTypes": [ "LineChart", "DiscreteBar", "SlopeChart" ], "yAxis": { "min": 0 }, "map": { "colorScale": { "baseColorScheme": "YlGnBu", "binningStrategy": "manual", "customNumericValues": [ 30, 40, 50, 60, 70, 80, 90, 100 ] } }, "selectedEntityNames": [ "Afghanistan", "Syria", "Uzbekistan", "United States", "Philippines", "China", "New Zealand", "Peru", "Uganda", "India" ] }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_advn_fe_zs": { "title": "Labor force with advanced education, female (% of female working-age population with advanced education)", "description_from_producer": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor\u2019s degree or equivalent education level, a master\u2019s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nStatistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.TLF.ADVN.FE.ZS (https://data.worldbank.org/indicator/SL.TLF.ADVN.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ADVN.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female working-age population with advanced education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female working-age population with advanced education" } }, "sl_tlf_advn_ma_zs": { "title": "Labor force with advanced education, male (% of male working-age population with advanced education)", "description_from_producer": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor\u2019s degree or equivalent education level, a master\u2019s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nStatistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.TLF.ADVN.MA.ZS (https://data.worldbank.org/indicator/SL.TLF.ADVN.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ADVN.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male working-age population with advanced education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male working-age population with advanced education" } }, "sl_tlf_advn_zs": { "title": "Labor force with advanced education (% of total working-age population with advanced education)", "description_from_producer": "The ratio of the labor force with advanced education to the working-age population with advanced education. Advanced education comprises short-cycle tertiary education, a bachelor\u2019s degree or equivalent education level, a master\u2019s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nStatistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.TLF.ADVN.ZS (https://data.worldbank.org/indicator/SL.TLF.ADVN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.ADVN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total working-age population with advanced education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total working-age population with advanced education" } }, "sl_tlf_basc_fe_zs": { "title": "Labor force with basic education, female (% of female working-age population with basic education)", "description_from_producer": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nStatistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.TLF.BASC.FE.ZS (https://data.worldbank.org/indicator/SL.TLF.BASC.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.BASC.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female working-age population with basic education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female working-age population with basic education" } }, "sl_tlf_basc_ma_zs": { "title": "Labor force with basic education, male (% of male working-age population with basic education)", "description_from_producer": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nStatistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.TLF.BASC.MA.ZS (https://data.worldbank.org/indicator/SL.TLF.BASC.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.BASC.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male working-age population with basic education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male working-age population with basic education" } }, "sl_tlf_basc_zs": { "title": "Labor force with basic education (% of total working-age population with basic education)", "description_from_producer": "The ratio of the labor force with basic education to the working-age population with basic education. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nStatistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.TLF.BASC.ZS (https://data.worldbank.org/indicator/SL.TLF.BASC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.BASC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total working-age population with basic education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total working-age population with basic education" } }, "sl_tlf_cact_fe_ne_zs": { "title": "Labor force participation rate, female (% of female population ages 15+) (national estimate)", "description_short": "Share of the female working-age population who are economically active (employed or unemployed). Figures refer to [national estimates](#dod:ilo-national-estimates) and may not be fully comparable across countries.", "description_from_producer": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- When defining the labor force, the working-age population includes all people above the legal working age for employment in each country. The ILO\u2019s guidelines define it as people aged 15 and older, but national definitions vary, depending on local laws. Some countries also set an upper age limit.\n- This data comes from national sources, typically labor force surveys, household surveys, or population censuses. Each country uses its own definitions and methods, so this data may not be directly comparable across countries and over time.\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.TLF.CACT.FE.NE.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.FE.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.CACT.FE.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Female labor force participation rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Female labor force participation rate", "note": "Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "National estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_cact_fe_zs": { "title": "Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)", "description_short": "Share of the female working-age population (ages 15 and over) who are economically active (employed or unemployed).", "description_from_producer": "The labor force participation rate is the labor force as a percent of the population ages 15 and older. The labor force is the sum of all persons of working age who are employed and those who are unemployed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate (LFPR) is calculated as follows:\n\n\nLFPR (%) = 100 x Labor force / population of a given age group, where the labor force is equal to employment plus unemployment.\n\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Population censuses are another major source of data on the labor force and its components.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\n\n\n\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\n\n\n\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- When defining the labor force, the definition of \u201cworking age\u201d varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.CACT.FE.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.CACT.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Female labor force participation rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Female labor force participation rate", "subtitle": "Share of the female working-age population (ages 15 and over) who are economically active (employed or unemployed).", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates", "hasMapTab": true, "tab": "map", "chartTypes": [ "LineChart", "DiscreteBar", "SlopeChart" ], "yAxis": { "min": 0 }, "map": { "colorScale": { "baseColorScheme": "YlGnBu", "binningStrategy": "manual", "customNumericValues": [ 0, 10, 20, 30, 40, 50, 60, 70, 1 ] } }, "selectedEntityNames": [ "Afghanistan", "Iraq", "Iran", "Pakistan", "India", "Saudi Arabia", "Italy", "Chile", "Spain", "New Zealand", "Mozambique", "United States", "Nigeria" ] }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_cact_fm_ne_zs": { "title": "Ratio of female to male labor force participation rate (%) (national estimate)", "description_from_producer": "Ratio of female to male labor force participation rate is the proportion of female labor force participation relative to male labor force participation. The labor force participation rate is the labor force as a percent of the population ages 15 and older. The labor force is the sum of all persons of working age who are employed and those who are unemployed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100. The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\n\n\n\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\n\n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nEstimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic. In many low-income countries women often work on farms or in other family enterprises without pay, and others work in or near their homes, mixing work and family activities during the day. In many high-income economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world. Labor force statistics by gender is important to monitor gender disparities in employment and unemployment patterns.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\n\n\n\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\n\n\n\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "International Labour Organization (ILOSTAT) and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB). Indicator SL.TLF.CACT.FM.NE.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.FM.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.CACT.FM.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_tlf_cact_fm_zs": { "title": "Ratio of female to male labor force participation rate (%) (modeled ILO estimate)", "description_short": "This ratio is calculated by dividing the labor force participation rate among women by the corresponding rate for men. The labor force participation rate is the share of the working-age population (ages 15 and over) who are economically active (employed or unemployed).", "description_from_producer": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Ratio of female to male labor force participation rate is calculated by dividing female labor force participation rate by male labor force participation rate and multiplying by 100. The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nEstimates of women in the labor force and employment are generally lower than those of men and are not comparable internationally, reflecting that demographic, social, legal, and cultural trends and norms determine whether women's activities are regarded as economic. In many low-income countries women often work on farms or in other family enterprises without pay, and others work in or near their homes, mixing work and family activities during the day. In many high-income economies, women have been increasingly acquiring higher education that has led to better-compensated, longer-term careers rather than lower-skilled, shorter-term jobs. However, access to good- paying occupations for women remains unequal in many occupations and countries around the world. Labor force statistics by gender is important to monitor gender disparities in employment and unemployment patterns.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "International Labour Organization (ILOEST/ILOSTAT) and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: estimates based on external database;\nStaff estimates, World Bank (WB). Indicator SL.TLF.CACT.FM.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.FM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.CACT.FM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Ratio of female to male labor force participation rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Ratio of female to male labor force participation rates", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming." }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_cact_ma_ne_zs": { "title": "Labor force participation rate, male (% of male population ages 15+) (national estimate)", "description_short": "Share of the male working-age population who are economically active (employed or unemployed). Figures refer to [national estimates](#dod:ilo-national-estimates) and may not be fully comparable across countries.", "description_from_producer": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- When defining the labor force, the working-age population includes all people above the legal working age for employment in each country. The ILO\u2019s guidelines define it as people aged 15 and older, but national definitions vary, depending on local laws. Some countries also set an upper age limit.\n- This data comes from national sources, typically labor force surveys, household surveys, or population censuses. Each country uses its own definitions and methods, so this data may not be directly comparable across countries and over time.\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.TLF.CACT.MA.NE.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.MA.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.CACT.MA.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Male labor force participation rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Male labor force participation rate", "note": "Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "National estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_cact_ma_zs": { "title": "Labor force participation rate, male (% of male population ages 15+) (modeled ILO estimate)", "description_short": "Share of the male working-age population (ages 15 and over) who are economically active (employed or unemployed).", "description_from_producer": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- When defining the labor force, the definition of \u201cworking age\u201d varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.CACT.MA.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.CACT.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Male labor force participation rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Male labor force participation rate", "subtitle": "Share of the male working-age population (ages 15 and over) who are economically active (employed or unemployed).", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates", "hasMapTab": true, "tab": "map", "chartTypes": [ "LineChart", "DiscreteBar", "SlopeChart" ], "yAxis": { "min": 0 }, "map": { "colorScale": { "baseColorScheme": "YlGnBu", "binningStrategy": "manual", "customNumericValues": [ 40, 50, 60, 70, 80, 90, 100 ] } }, "selectedEntityNames": [ "Afghanistan", "Syria", "United States", "China", "New Zealand", "Peru", "Uganda", "India" ] }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_cact_ne_zs": { "title": "Labor force participation rate, total (% of total population ages 15+) (national estimate)", "description_short": "Share of the working-age population who are economically active (employed or unemployed). Figures refer to [national estimates](#dod:ilo-national-estimates) and may not be fully comparable across countries.", "description_from_producer": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\n\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- When defining the labor force, the working-age population includes all people above the legal working age for employment in each country. The ILO\u2019s guidelines define it as people aged 15 and older, but national definitions vary, depending on local laws. Some countries also set an upper age limit.\n- This data comes from national sources, typically labor force surveys, household surveys, or population censuses. Each country uses its own definitions and methods, so this data may not be directly comparable across countries and over time.\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.TLF.CACT.NE.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.CACT.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Labor force participation rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Labor force participation rate", "note": "Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "National estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_cact_zs": { "title": "Labor force participation rate, total (% of total population ages 15+) (modeled ILO estimate)", "description_short": "Share of the working-age population (ages 15 and over) who are economically active (employed or unemployed).", "description_from_producer": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The labor force participation rate shows the share of working-age people who are either employed (working for pay or profit) or unemployed (not working, but actively looking for work and available to start). This indicator shows the share of the population that is economically active.\n- People who are not seeking work or are not available, such as students, retired people, or unpaid caregivers, are excluded as they are considered to be outside the labor force.\n- When defining the labor force, the definition of \u201cworking age\u201d varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.CACT.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.CACT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Labor force participation rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Labor force participation rate", "subtitle": "Share of the working-age population (ages 15 and over) who are economically active (employed or unemployed).", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates", "hasMapTab": true, "tab": "map", "chartTypes": [ "LineChart", "DiscreteBar", "SlopeChart" ], "yAxis": { "min": 0 }, "map": { "colorScale": { "baseColorScheme": "YlGnBu", "binningStrategy": "manual", "customNumericValues": [ 20, 30, 40, 50, 60, 70, 80, 90, 100 ] } }, "selectedEntityNames": [ "Afghanistan", "Syria", "Uzbekistan", "United States", "Philippines", "China", "New Zealand", "Peru", "Uganda", "India" ] }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_intm_fe_zs": { "title": "Labor force with intermediate education, female (% of female working-age population with intermediate education)", "description_from_producer": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nStatistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.TLF.INTM.FE.ZS (https://data.worldbank.org/indicator/SL.TLF.INTM.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.INTM.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female working-age population with intermediate education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female working-age population with intermediate education" } }, "sl_tlf_intm_ma_zs": { "title": "Labor force with intermediate education, male (% of male working-age population with intermediate education)", "description_from_producer": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nStatistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.TLF.INTM.MA.ZS (https://data.worldbank.org/indicator/SL.TLF.INTM.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.INTM.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male working-age population with intermediate education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male working-age population with intermediate education" } }, "sl_tlf_intm_zs": { "title": "Labor force with intermediate education (% of total working-age population with intermediate education)", "description_from_producer": "The ratio of the labor force with intermediate education to the working-age population with intermediate education. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The labor force participation rate is the labor force as a percent of the working-age population. The labor force is the sum of all persons of working age who are employed and those who are unemployed. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nStatistics on levels and trends in educational attainment of the labor force can: (a) provide an indication of the capacity of countries to achieve important social and economic goals; (b) give insights into the broad skill structure of the labor force; (c) highlight the need to promote investments in education for different population groups; (d) support analysis of the influence of skill levels on economic outcomes and the success of different policies in raising the educational level of the workforce; (e) give an indication of the degree of inequality in the distribution of education resources between groups of the population, particularly between men and women, and within and between countries; and (f) provide an indication of the skills of the existing labor force, with a view to discovering untapped potential.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.TLF.INTM.ZS (https://data.worldbank.org/indicator/SL.TLF.INTM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.INTM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total working-age population with intermediate education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total working-age population with intermediate education" } }, "sl_tlf_part_fe_zs": { "title": "Part time employment, female (% of total female employment)", "description_from_producer": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\n\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\n\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n### Development relevance:\nPart-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\n\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\n\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force.\n\n### Other notes:\nRelevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does.", "origins": [ { "producer": "Wages and Working Time Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.TLF.PART.FE.ZS (https://data.worldbank.org/indicator/SL.TLF.PART.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.PART.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total female employment", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total female employment" } }, "sl_tlf_part_ma_zs": { "title": "Part time employment, male (% of total male employment)", "description_from_producer": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\n\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\n\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n### Development relevance:\nPart-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\n\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\n\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force.\n\n### Other notes:\nRelevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does.", "origins": [ { "producer": "Wages and Working Time Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.TLF.PART.MA.ZS (https://data.worldbank.org/indicator/SL.TLF.PART.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.PART.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total male employment", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total male employment" } }, "sl_tlf_part_zs": { "title": "Part time employment, total (% of total employment)", "description_from_producer": "Part-time employment rate represents the percentage of employment that is part time. Part time employment in this series is based on a common definition of less than 35 actual weekly hours worked.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are typically the preferred source of information on hours of work. Such surveys can be designed to cover virtually the entire non-institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders.\n\nOther types of household surveys could also be used as sources of data on hours of work, if they have an appropriate module on the topic.\n\nIn the absence of a labor force survey or other types of household surveys with a module on working time, an establishment survey can be used as a source of statistics on hours of work. However, the statistics derived from establishments surveys would typically not refer to the whole employed population but only to employees (and often only to formal sector employees or non-agricultural formal sector employees).\nStatistical concept(s): Employment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n### Development relevance:\nPart-time employment has been seen as an instrument to increase labor supply. Indeed, as part-time work may offer the chance of a better balance between working life and family responsibilities, and suits workers who prefer shorter working hours and more time for their private life, it may allow more working-age persons to actually join the labor force. Also, policy-makers have promoted part-time work in an attempt to redistribute working time in countries of high unemployment, thus lowering politically sensitive unemployment rates without requiring an increase in the total number of hours worked.\n\n\n\nPart-time employment, however, is not always a choice. While flexibility may be one advantage of part-time work, disadvantages may exist in comparison with colleagues who work full time. Since the early 1990s, most OECD countries have introduced measures to improve the quality of part-time work, for example with respect to social benefits for part-time workers in line with those of full-time workers. Nevertheless, occupational segregation between part-time and full-time work remains an issue in most countries as it limits the occupational choices of part-time workers.\n\n\n\nLooking at part-time employment by sex is useful to see the extent to which the female labor force is more likely to work part time than the male labor force.\n\n### Other notes:\nRelevance to gender indicator: More and more women are working part-time and one of the concern is that part time work does not provide the stability that full time work does.", "origins": [ { "producer": "Wages and Working Time Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Wages and Working Time Statistics database (COND), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.TLF.PART.ZS (https://data.worldbank.org/indicator/SL.TLF.PART.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.PART.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total employment", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total employment" } }, "sl_tlf_totl_fe_zs": { "title": "Labor force, female (% of total labor force)", "description_short": "Share of the working-age population economically active (employed or unemployed) who are female.", "description_from_producer": "Female labor force as a percentage of the total show the extent to which women are active in the labor force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the sum of all persons of working age who are employed and those who are unemployed. The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable\u2014at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.", "description_key": "- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "International Labour Organization and UN Population Division, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Labour Organization (ILO), type: estimates based on external database;\nUnited Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SL.TLF.TOTL.FE.ZS (https://data.worldbank.org/indicator/SL.TLF.TOTL.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.TOTL.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Share of the labor force that is female", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_tlf_totl_in": { "title": "Labor force, total", "description_from_producer": "Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are based on labor force participation rates and population data from International Labour Organization and United Nations Population Division. The labor force participation rates are part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The standard definition of unemployed persons is those individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or voluntarily left work. In addition, persons who did not look for work but have an arrangement for a future job are also counted as unemployed. Still, some unemployment is unavoidable\u2014at any time, some workers are temporarily unemployed between jobs as employers look for the right workers and workers search for better jobs. The labor force or the economically active portion of the population serves as the base for this indicator, not the total population.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.", "origins": [ { "producer": "International Labour Organization and UN Population Division, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Labour Organization (ILO), type: estimates based on external database;\nUnited Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SL.TLF.TOTL.IN (https://data.worldbank.org/indicator/SL.TLF.TOTL.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.TOTL.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sl_uem_1524_fe_ne_zs": { "title": "Unemployment, youth female (% of female labor force ages 15-24) (national estimate)", "description_from_producer": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to \u201cregistered unemployed\u201d only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n### Limitations and exceptions:\nThe criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\n\n\n\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men.\n\n\n\n\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.UEM.1524.FE.NE.ZS (https://data.worldbank.org/indicator/SL.UEM.1524.FE.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.1524.FE.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_uem_1524_fe_zs": { "title": "Unemployment, youth female (% of female labor force ages 15-24) (modeled ILO estimate)", "description_from_producer": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\n\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\n\n\n\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n### Limitations and exceptions:\nThe criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\n\n\n\n\n\n\n\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men.\n\n\n\n\n\n\n\n\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.UEM.1524.FE.ZS (https://data.worldbank.org/indicator/SL.UEM.1524.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.1524.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_uem_1524_ma_ne_zs": { "title": "Unemployment, youth male (% of male labor force ages 15-24) (national estimate)", "description_from_producer": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to \u201cregistered unemployed\u201d only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n### Limitations and exceptions:\nThe criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men.\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.UEM.1524.MA.NE.ZS (https://data.worldbank.org/indicator/SL.UEM.1524.MA.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.1524.MA.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_uem_1524_ma_zs": { "title": "Unemployment, youth male (% of male labor force ages 15-24) (modeled ILO estimate)", "description_from_producer": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n### Limitations and exceptions:\nThe criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men.\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.UEM.1524.MA.ZS (https://data.worldbank.org/indicator/SL.UEM.1524.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.1524.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "modeled ILO estimate", "display": { "unit": "modeled ILO estimate" } }, "sl_uem_1524_ne_zs": { "title": "Unemployment, youth total (% of total labor force ages 15-24) (national estimate)", "description_from_producer": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to \u201cregistered unemployed\u201d only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n### Limitations and exceptions:\nThe criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men.\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.UEM.1524.NE.ZS (https://data.worldbank.org/indicator/SL.UEM.1524.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.1524.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_uem_1524_zs": { "title": "Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)", "description_short": "Share of the [labor force](#dod:labor-force) aged 15-24 without work, but actively looking for a job and available to start soon.", "description_from_producer": "Youth unemployment refers to the share of the labor force ages 15-24 without work but available for and seeking employment.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n### Limitations and exceptions:\nThe criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men.\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The unemployment rate measures the share of the [labor force](#dod:labor-force) that is without a job but actively looking for work and available to start soon. It is one of the most widely used indicators of labor market conditions across countries and over time.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.UEM.1524.ZS (https://data.worldbank.org/indicator/SL.UEM.1524.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.1524.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Unemployment rate, ages 15-24", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Youth unemployment rate", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_uem_advn_fe_zs": { "title": "Unemployment with advanced education, female (% of female labor force with advanced education)", "description_from_producer": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor\u2019s degree or equivalent education level, a master\u2019s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nFocusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the \u201cbrain drain\u201d, whereby educated professionals migrate in order to find employment in other areas of the world.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.UEM.ADVN.FE.ZS (https://data.worldbank.org/indicator/SL.UEM.ADVN.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.ADVN.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female labor force with advanced education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female labor force with advanced education" } }, "sl_uem_advn_ma_zs": { "title": "Unemployment with advanced education, male (% of male labor force with advanced education)", "description_from_producer": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor\u2019s degree or equivalent education level, a master\u2019s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nFocusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the \u201cbrain drain\u201d, whereby educated professionals migrate in order to find employment in other areas of the world.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.UEM.ADVN.MA.ZS (https://data.worldbank.org/indicator/SL.UEM.ADVN.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.ADVN.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male labor force with advanced education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male labor force with advanced education" } }, "sl_uem_advn_zs": { "title": "Unemployment with advanced education (% of total labor force with advanced education)", "description_from_producer": "The percentage of the labor force with an advanced level of education who are unemployed. Advanced education comprises short-cycle tertiary education, a bachelor\u2019s degree or equivalent education level, a master\u2019s degree or equivalent education level, or doctoral degree or equivalent education level according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nFocusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the \u201cbrain drain\u201d, whereby educated professionals migrate in order to find employment in other areas of the world.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.UEM.ADVN.ZS (https://data.worldbank.org/indicator/SL.UEM.ADVN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.ADVN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total labor force with advanced education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total labor force with advanced education" } }, "sl_uem_basc_fe_zs": { "title": "Unemployment with basic education, female (% of female labor force with basic education)", "description_from_producer": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nFocusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the \u201cbrain drain\u201d, whereby educated professionals migrate in order to find employment in other areas of the world.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.UEM.BASC.FE.ZS (https://data.worldbank.org/indicator/SL.UEM.BASC.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.BASC.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female labor force with basic education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female labor force with basic education" } }, "sl_uem_basc_ma_zs": { "title": "Unemployment with basic education, male (% of male labor force with basic education)", "description_from_producer": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nFocusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the \u201cbrain drain\u201d, whereby educated professionals migrate in order to find employment in other areas of the world.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.UEM.BASC.MA.ZS (https://data.worldbank.org/indicator/SL.UEM.BASC.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.BASC.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male labor force with basic education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male labor force with basic education" } }, "sl_uem_basc_zs": { "title": "Unemployment with basic education (% of total labor force with basic education)", "description_from_producer": "The percentage of the labor force with a basic level of education who are unemployed. Basic education comprises primary education or lower secondary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nFocusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the \u201cbrain drain\u201d, whereby educated professionals migrate in order to find employment in other areas of the world.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.UEM.BASC.ZS (https://data.worldbank.org/indicator/SL.UEM.BASC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.BASC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total labor force with basic education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total labor force with basic education" } }, "sl_uem_intm_fe_zs": { "title": "Unemployment with intermediate education, female (% of female labor force with intermediate education)", "description_from_producer": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nFocusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the \u201cbrain drain\u201d, whereby educated professionals migrate in order to find employment in other areas of the world.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.UEM.INTM.FE.ZS (https://data.worldbank.org/indicator/SL.UEM.INTM.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.INTM.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female labor force with intermediate education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female labor force with intermediate education" } }, "sl_uem_intm_ma_zs": { "title": "Unemployment with intermediate education, male (% of male labor force with intermediate education)", "description_from_producer": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nFocusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the \u201cbrain drain\u201d, whereby educated professionals migrate in order to find employment in other areas of the world.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.UEM.INTM.MA.ZS (https://data.worldbank.org/indicator/SL.UEM.INTM.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.INTM.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male labor force with intermediate education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male labor force with intermediate education" } }, "sl_uem_intm_zs": { "title": "Unemployment with intermediate education (% of total labor force with intermediate education)", "description_from_producer": "The percentage of the labor force with an intermediate level of education who are unemployed. Intermediate education comprises upper secondary or post-secondary non tertiary education according to the International Standard Classification of Education 2011 (ISCED 2011).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Labor force surveys are the preferred source of statistics on employment by educational attainment, since they provide information on both the labor market situation of individuals and their level of educational attainment. Such surveys can be designed to cover virtually the entire non- institutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force (and thus, the working-age population) in a coherent framework. Other types of household surveys and population censuses could also be used as sources of data on employment by educational attainment. The information obtained from such sources may however be less reliable since they do not typically allow for detailed probing on the labor market activities of the respondents.\nStatistical concept(s): The unemployment rate conveys the number of persons who are unemployed as a percent of the labour force (i.e., the employed plus the unemployed). The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Data disaggregated by level of education are provided on the highest level of education completed, classified according to the International Standard Classification of Education (ISCED). Data may have been regrouped from national classifications, which may not be strictly compatible with ISCED.\n\n### Development relevance:\nFocusing on the educational characteristics of the unemployed can aid to shed light on how significant long-term events in a country, such as skill-based technological change, increased trade openness or shifts in the sectoral structure of the economy, alter the experience of high- and low-skilled workers in the labor market. The information provided can have important implications for both employment and education policy. To the extent that persons with low education levels are at a higher risk of becoming unemployed, the political reaction may be either to seek to increase their education level or to create more low-skilled occupations within the country. Alternatively, a higher share of unemployment among persons with higher education could indicate a lack of sufficient professional and high-level technical jobs. In many countries, qualified jobseekers are being forced to accept employment below their skill level. Where the supply of qualified workers outpaces the increase in the number of professional and technical employment opportunities, high levels of skills-related underemployment are inevitable. A possible consequence of the presence of highly educated unemployed in a country is the \u201cbrain drain\u201d, whereby educated professionals migrate in order to find employment in other areas of the world.", "origins": [ { "producer": "Education and Mismatch Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Education and Mismatch Indicators database (EMI), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: March 30, 2026. Indicator SL.UEM.INTM.ZS (https://data.worldbank.org/indicator/SL.UEM.INTM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.INTM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total labor force with intermediate education", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total labor force with intermediate education" } }, "sl_uem_neet_fe_me_zs": { "title": "Share of youth not in education, employment or training, female (% of female youth population) (modeled ILO estimate)", "description_from_producer": "The share of youth not in education, employment or training (also known as \u201cthe NEET rate\u201d) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The youth NEET rate is calculated as follows: NEET rate = (Youth \u2013 Youth in employment \u2013 Youth not in employment but in education or training) / Youth x 100.\n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) \u2013 (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100.\n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator.\n\n### Development relevance:\nUnemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets.\n\n### Limitations and exceptions:\nWhen differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.UEM.NEET.FE.ME.ZS (https://data.worldbank.org/indicator/SL.UEM.NEET.FE.ME.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.NEET.FE.ME.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sl_uem_neet_fe_zs": { "title": "Share of youth not in education, employment or training, female (% of female youth population)", "description_from_producer": "The share of youth not in education, employment or training (also known as \u201cthe NEET rate\u201d) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The youth NEET rate is calculated as follows: NEET rate = (Youth \u2013 Youth in employment \u2013 Youth not in employment but in education or training) / Youth x 100.\n\n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) \u2013 (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100.\n\n\n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator.\n\n### Development relevance:\nUnemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementary information to labour force participation rates and unemployment rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets.\n\n### Limitations and exceptions:\nWhen differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.UEM.NEET.FE.ZS (https://data.worldbank.org/indicator/SL.UEM.NEET.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.NEET.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female youth population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female youth population" } }, "sl_uem_neet_ma_me_zs": { "title": "Share of youth not in education, employment or training, male (% of male youth population) (modeled ILO estimate)", "description_from_producer": "The share of youth not in education, employment or training (also known as \u201cthe NEET rate\u201d) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The youth NEET rate is calculated as follows: NEET rate = (Youth \u2013 Youth in employment \u2013 Youth not in employment but in education or training) / Youth x 100.\n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) \u2013 (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100.\n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator.\n\n### Development relevance:\nUnemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementary information to labour force participation rates and unemployment rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets.\n\n### Limitations and exceptions:\nWhen differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.UEM.NEET.MA.ME.ZS (https://data.worldbank.org/indicator/SL.UEM.NEET.MA.ME.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.NEET.MA.ME.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sl_uem_neet_ma_zs": { "title": "Share of youth not in education, employment or training, male (% of male youth population)", "description_from_producer": "The share of youth not in education, employment or training (also known as \u201cthe NEET rate\u201d) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The youth NEET rate is calculated as follows: NEET rate = (Youth \u2013 Youth in employment \u2013 Youth not in employment but in education or training) / Youth x 100.\n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) \u2013 (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100.\n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator.\n\n### Development relevance:\nUnemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets.\n\n### Limitations and exceptions:\nWhen differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.UEM.NEET.MA.ZS (https://data.worldbank.org/indicator/SL.UEM.NEET.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.NEET.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male youth population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male youth population" } }, "sl_uem_neet_me_zs": { "title": "Share of youth not in education, employment or training, total (% of youth population) (modeled ILO estimate)", "description_from_producer": "The share of youth not in education, employment or training (also known as \u201cthe NEET rate\u201d) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The youth NEET rate is calculated as follows: NEET rate = (Youth \u2013 Youth in employment \u2013 Youth not in employment but in education or training) / Youth x 100.\n\n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) \u2013 (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100.\n\n\n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator.\n\n### Development relevance:\nUnemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementray information to labour force participation rates and unemploymenent rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets.\n\n### Limitations and exceptions:\nWhen differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.UEM.NEET.ME.ZS (https://data.worldbank.org/indicator/SL.UEM.NEET.ME.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.NEET.ME.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sl_uem_neet_zs": { "title": "Share of youth not in education, employment or training, total (% of youth population)", "description_from_producer": "The share of youth not in education, employment or training (also known as \u201cthe NEET rate\u201d) conveys the number of young persons not in education, employment or training as a percentage of the total youth population. Youth not in education are those who were neither enrolled in school nor in a formal training program (e.g. vocational training). For the purposes of this indicator, youth is defined as all persons between the ages of 15 and 24 (inclusive).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The youth NEET rate is calculated as follows: NEET rate = (Youth \u2013 Youth in employment \u2013 Youth not in employment but in education or training) / Youth x 100.\n\n\nIt is important to note here that youth both in employment and education or training simultaneously should not be double counted when subtracted from the total number of youth. The formula can also be expressed as: NEET rate = [(Unemployed youth + Youth outside the labor force) \u2013 (Unemployed youth in education or training + Youth outside the labor force in education or training)] / Youth x 100.\n\n\n\n\n\nThe calculation of this indicator requires having reliable information on both the labor market status and the participation in education or training of youth. The quality of such information is heavily dependent on the questionnaire design, the sample size and design and the accuracy of respondents' answers. To avoid misinterpreting this indicator, it is important to bear in mind that it is composed of two different sub-groups (unemployed youth not in education or training and youth outside the labor force not in education or training). The prevalence and composition of each sub-group would have policy implications, and thus should also be considered when analyzing the NEET rate.\n\n\n\n\n\nThe preferred official national data source for this indicator is a household-based labor force survey. In the absence of a labor force survey, a population census and/or other type of household survey with an appropriate employment module may be used to obtain the required data.\nStatistical concept(s): For the purposes of these indicators, persons will be considered in education if they are in formal or non-formal education, but excluding informal learning.\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\n\n\nPersons are considered to be in training if they are in a nonacademic learning activity through which they acquire specific skills intended for vocational or technical jobs. Vocational training prepares trainees for jobs that are based on manual or practical activities, and for skilled operative jobs, both blue and white collar related to a specific trade, occupation or vocation. Technical training on the other hand imparts learning that can be applied in intermediate-level jobs, in particular those of technicians and middle managers. The coverage of vocational and technical training includes only programmes that are solely school-based vocational and technical training. Employer-based training is, by definition, excluded from the scope of this indicator.\n\n### Development relevance:\nUnemployment and total employment are the broadest indicators of economic activity as reflected by the labor market. The International Labour Organization(ILO) defines the unemployed as members of the economically active population who are without work but available for and seeking work, including people who have lost their jobs or who have voluntarily left work. Some unemployment is unavoidable. At any time some workers are temporarily unemployed - between jobs as employers look for the right workers and workers search for better jobs. Such unemployment, often called frictional unemployment, results from the normal operation of labor markets.\n\n\n\n\n\nYouth unemployment is an important policy issue for many economies. Young men and women today face increasing uncertainty in their hopes of undergoing a satisfactory transition in the labour market, and this uncertainty and disillusionment can, in turn, have damaging effects on individuals, communities, economies and society at large. Unemployed or underemployed youth are less able to contribute effectively to national development and have fewer opportunities to exercise their rights as citizens. They have less to spend as consumers, less to invest as savers and often have no \"voice\" to bring about change in their lives and communities. Widespread youth unemployment and underemployment also prevents companies and countries from innovating and developing competitive advantages based on human capital investment, thus undermining future prospects.\n\n\n\n\n\nThe NEET group is particularly at risk of both labour market and social exclusion, because this group is neither improving their future employability through investment in skills nor gaining experience through employment, . In addition, the NEET group is already in a disadvantaged position due to lower levels of education and lower household incomes. In view of the fact that the NEET group includes unemployed youth as well as economically inactive youth, the NEET rate provides important complementary information to labour force participation rates and unemployment rates. For example, if youth participation rates decrease during an economic downturn due to discouragement, this may be reflected in an upward movement in the NEET rate. More generally, a high NEET rate and a low youth unemployment may indicate significant discouragement of young people. A high NEET rate for young women suggests their engagement in household chores, and/or the presence of strong institutional barriers limiting female participation in labour markets.\n\n### Limitations and exceptions:\nWhen differing from international standards, the operational criteria used to define employment and the participation in education or training will naturally affect the comparability of the resulting statistics, as will the coverage of the source of statistics (geographical coverage, population coverage, age coverage, etc.). NEET rates are calculated preferably for youth defined as persons aged 15 to 24, but when studying these rates it is important to keep in mind that not all persons complete their education by the age of 24.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.UEM.NEET.ZS (https://data.worldbank.org/indicator/SL.UEM.NEET.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.NEET.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of youth population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of youth population" } }, "sl_uem_totl_fe_ne_zs": { "title": "Unemployment, female (% of female labor force) (national estimate)", "description_from_producer": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to \u201cregistered unemployed\u201d only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n### Limitations and exceptions:\nThe criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men.\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.UEM.TOTL.FE.NE.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.FE.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.TOTL.FE.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_uem_totl_fe_zs": { "title": "Unemployment, female (% of female labor force) (modeled ILO estimate)", "description_short": "Share of the female [labor force](#dod:labor-force) without work, but actively looking for a job and available to start soon.", "description_from_producer": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n### Limitations and exceptions:\nThe criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men.\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The unemployment rate measures the share of the [labor force](#dod:labor-force) that is without a job but actively looking for work and available to start soon. It is one of the most widely used indicators of labor market conditions across countries and over time.\n- When defining the labor force, the definition of \u201cworking age\u201d varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.UEM.TOTL.FE.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.TOTL.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Female unemployment rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Female unemployment rate", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_uem_totl_ma_ne_zs": { "title": "Unemployment, male (% of male labor force) (national estimate)", "description_from_producer": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\n\n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to \u201cregistered unemployed\u201d only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n### Limitations and exceptions:\nWhile the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment \u2013 whether it is cyclical and short-term or structural and long-term \u2013 which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.UEM.TOTL.MA.NE.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.MA.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.TOTL.MA.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "national estimate", "display": { "unit": "national estimate" } }, "sl_uem_totl_ma_zs": { "title": "Unemployment, male (% of male labor force) (modeled ILO estimate)", "description_short": "Share of the male [labor force](#dod:labor-force) without work, but actively looking for a job and available to start soon.", "description_from_producer": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n### Limitations and exceptions:\nThe criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men.\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The unemployment rate measures the share of the [labor force](#dod:labor-force) that is without a job but actively looking for work and available to start soon. It is one of the most widely used indicators of labor market conditions across countries and over time.\n- When defining the labor force, the definition of \u201cworking age\u201d varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.UEM.TOTL.MA.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.TOTL.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Male unemployment rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Male unemployment rate", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_uem_totl_ne_zs": { "title": "Unemployment, total (% of total labor force) (national estimate)", "description_short": "Share of the [labor force](#dod:labor-force) without work, but actively looking for a job and available to start soon. Figures refer to [national estimates](#dod:ilo-national-estimates) and may not be fully comparable across countries.", "description_from_producer": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment. Definitions of labor force and unemployment differ by country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\nHousehold labor force surveys are generally the most comprehensive and comparable sources for unemployment statistics. Other possible sources include population censuses and official estimates. Administrative records such as employment office records and social insurance statistics are also sources of unemployment statistics; however, coverage in such sources is limited to \u201cregistered unemployed\u201d only.\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n### Limitations and exceptions:\nThe criteria for people considered to be seeking work, and the treatment of people temporarily laid off or seeking work for the first time, vary across countries. In many cases it is especially difficult to measure employment and unemployment in agriculture. The timing of a survey can maximize the effects of seasonal unemployment in agriculture. And informal sector employment is difficult to quantify where informal activities are not tracked.\n\nThere may be also persons not currently in the labour market who want to work but do not actively \"seek\" work because they view job opportunities as limited, or because they have restricted labour mobility, or face discrimination, or structural, social or cultural barriers. The exclusion of people who want to work but are not seeking work (often called the \"hidden unemployed\" or \"discouraged workers\") is a criterion that will affect the unemployment count of both women and men.\n\nHowever, women tend to be excluded from the count for various reasons. Women suffer more from discrimination and from structural, social, and cultural barriers that impede them from seeking work. Also, women are often responsible for the care of children and the elderly and for household affairs. They may not be available for work during the short reference period, as they need to make arrangements before starting work. Further, women are considered to be employed when they are working part-time or in temporary jobs, despite the instability of these jobs or their active search for more secure employment.\n\n### Other notes:\nThe series for ILO estimates is also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The unemployment rate measures the share of the [labor force](#dod:labor-force) that is without a job but actively looking for work and available to start soon. It is one of the most widely used indicators of labor market conditions across countries and over time.\n- When defining the labor force, the working-age population includes all people above the legal working age for employment in each country. The ILO\u2019s guidelines define it as people aged 15 and older, but national definitions vary, depending on local laws. Some countries also set an upper age limit.\n- This data comes from national sources, typically labor force surveys, household surveys, or population censuses. Each country uses its own definitions and methods, so this data may not be directly comparable across countries and over time.\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.UEM.TOTL.NE.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.TOTL.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Unemployment rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Unemployment rate", "note": "Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "National estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_uem_totl_zs": { "title": "Unemployment, total (% of total labor force) (modeled ILO estimate)", "description_short": "Share of the [labor force](#dod:labor-force) without work, but actively looking for a job and available to start soon.", "description_from_producer": "Unemployment refers to the share of the labor force that is without work but available for and seeking employment.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The unemployment rate is calculated by expressing the number of unemployed persons as a percentage of the total number of persons in the labor force. The labor force (formerly known as the economically active population) is the sum of the number of persons employed and the number of persons unemployed.\n\n\n\n\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labor market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programs, who on that basis, were \u201cnot in employment\u201d, not \u201ccurrently available\u201d and did not \u201cseek employment\u201d because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons \u201cnot in employment\u201d who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave.\n\n\n\n\n\n\n\nEmployment comprises all persons of working age who during a specified brief period, such as one week or one day, were in the following categories: a) paid employment (whether at work or with a job but not at work); or b) self-employment (whether at work or with an enterprise but not at work).\n\n\n\nThe working-age population is the population above the legal working age, but for statistical purposes it comprises all persons above a specified minimum age threshold for which an inquiry on economic activity is made. To promote international comparability, the working-age population is often defined as all persons aged 15 and older, but this may vary from country to country based on national laws and practices (some countries also apply an upper age limit).\n\n### Development relevance:\nThe unemployment rate is a useful measure of the underutilization of the labor supply. It reflects the inability of an economy to generate employment for those persons who want to work but are not doing so, even though they are available for employment and actively seeking work. It is thus seen as an indicator of the efficiency and effectiveness of an economy to absorb its labor force and of the performance of the labor market.\n\n\n\n\n\n\n\nGiven its usefulness in conveying valuable information on a country\u2019s labor market situation and the fact that it is widely recognized as a headline labor market indicator, it was included as one of the indicators to measure progress towards the achievement of the Sustainable Development Goals (SDG), under Goal 8 (Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all).\n\n### Limitations and exceptions:\nWhile the unemployment rate may be considered the most informative labour market indicator, reflecting the general performance of the labour market and the economy as a whole, it should not be interpreted as a measure of economic hardship or of well-being. When based on the internationally-recommended standards, the unemployment rate simply reflects the proportion of the labour force that does not have a job but is available and actively looking for work. It says nothing about the economic resources of unemployed workers or their family members. Its use should, therefore, be limited to serving as a measurement of the utilization of labour and an indication of the failure to find work. Other measures, including income-related indicators, would be needed to evaluate economic hardship. An additional criticism of the aggregate unemployment measure is that it masks information on the composition of the jobless population and therefore misses out on the particularities of the education level, ethnic origin, socio-economic background, work experience, etc. of the unemployed. Moreover, the unemployment rate says nothing about the type of unemployment \u2013 whether it is cyclical and short-term or structural and long-term \u2013 which is a critical issue for policy makers in the development of their policy responses, especially given that structural unemployment cannot be addressed by boosting market demand only.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- The unemployment rate measures the share of the [labor force](#dod:labor-force) that is without a job but actively looking for work and available to start soon. It is one of the most widely used indicators of labor market conditions across countries and over time.\n- When defining the labor force, the definition of \u201cworking age\u201d varies across countries, depending on national laws and practices. In the ILO modeled estimates shown here, this is harmonized to refer to people aged 15 and older.\n- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.UEM.TOTL.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "name": "Unemployment rate", "numDecimalPlaces": 1, "tolerance": 5, "shortUnit": "%", "unit": "%" }, "presentation": { "grapher_config": { "title": "Unemployment rate", "note": "All figures correspond to [modeled ILO estimates](#dod:ilo-modelled-estimates). Employment is defined in line with the [13th ICLS](#dod:13-icls) and includes the production of goods for own use, such as subsistence farming.", "variantName": "Modeled estimates" }, "attribution_short": "ILO", "topic_tags": [ "Work & Employment" ] } }, "sl_wag_0714_fe_zs": { "title": "Children in employment, wage workers, female (% of female children in employment, ages 7-14)", "description_from_producer": "Wage workers (also known as employees) are people who hold explicit (written or oral) or implicit employment contracts that provide basic remuneration that does not depend directly on the revenue of the unit for which they work.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.WAG.0714.FE.ZS (https://data.worldbank.org/indicator/SL.WAG.0714.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.WAG.0714.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female children in employment, ages 7-14" } }, "sl_wag_0714_ma_zs": { "title": "Children in employment, wage workers, male (% of male children in employment, ages 7-14)", "description_from_producer": "Wage workers (also known as employees) are people who hold explicit (written or oral) or implicit employment contracts that provide basic remuneration that does not depend directly on the revenue of the unit for which they work.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.WAG.0714.MA.ZS (https://data.worldbank.org/indicator/SL.WAG.0714.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.WAG.0714.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male children in employment, ages 7-14" } }, "sl_wag_0714_zs": { "title": "Children in employment, wage workers (% of children in employment, ages 7-14)", "description_from_producer": "Wage workers (also known as employees) are people who hold explicit (written or oral) or implicit employment contracts that provide basic remuneration that does not depend directly on the revenue of the unit for which they work.\n\n### Statistical concept and methodology:\nMethodology: Data are from household surveys by the International Labor Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. The surveys yield data on education, employment, health, expenditure, and consumption indicators related to children's work. Since children's work is captured in the sense of \"economic activity,\" the data refer to children in employment, a broader concept than child labor (see ILO 2009a for details on this distinction).\n\nHousehold survey data generally include information on work type - for example, whether a child is working for payment in cash or in kind or is involved in unpaid work, working for someone who is not a member of the household, or involved in any type of family work (on the farm or in a business).\n\nIn line with the definition of economic activity adopted by the 13th International Conference of Labour Statisticians, the threshold set by the 1993 UN System of National Accounts for classifying a person as employed is to have been engaged at least one hour in any activity relating to the production of goods and services during the reference period. Children seeking work are thus excluded. Economic activity covers all market production and certain nonmarket production, including production of goods for own use. It excludes unpaid household services (commonly called \"household chores\") - that is, the production of domestic and personal services by household members for a household's own consumption.\n\nCountry surveys define the ages for child labor as 5-17. The data here have been recalculated to present statistics for children ages 7-14.\n\n### Development relevance:\nIn most countries more boys are involved in employment, or the gender difference is small. However, girls are often more present in hidden or underreported forms of employment such as domestic service, and in almost all societies girls bear greater responsibility for household chores in their own homes, work that lies outside the System of National Accounts production boundary and is thus not considered in estimates of children's employment.\n\n### Limitations and exceptions:\nAlthough efforts are made to harmonize the definition of employment and the questions on employment in survey questionnaires, significant differences remain in the survey instruments that collect data on children in employment and in the sampling design underlying the surveys. Differences exist not only across different household surveys in the same country but also across the same type of survey carried out in different countries, so estimates of working children are not fully comparable across countries. In addition, the shares of three categories (self-employed workers, wage workers, and unpaid family workers) may not add up to 100 percent because of a residual category not included.", "origins": [ { "producer": "International Labour Organization, UNICEF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Understanding Children's Work, International Labour Organization (ILO);\nUN Children's Fund (UNICEF), note: Understanding Children's Work;\nWorld Bank (WB), note: Understanding Children's Work. Indicator SL.WAG.0714.ZS (https://data.worldbank.org/indicator/SL.WAG.0714.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.WAG.0714.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children in employment, ages 7-14", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children in employment, ages 7-14" } }, "sm_pop_asys_ea": { "title": "Asylum-seekers by country or territory of asylum", "description_from_producer": "Asylum-seekers are individuals who have sought international protection and whose claims for refugee status have not yet been determined. This includes those who are in various stages of the asylum process, such as initial application, appeal, or awaiting final decision. In specific contexts, asylum-seekers may also include those who are seeking protection under complementary forms of protection, and those enjoying temporary protection, but whose claims are still under consideration.\n\n### Statistical concept and methodology:\nMethodology: Data collection on asylum-seekers is primarily handled through administrative records maintained by national authorities responsible for processing asylum claims. These records capture various stages of the asylum process, including the number of new applications, repeat applications, and cases that are pending resolution.\n\n\n\nThe methodology for collecting asylum-seeker data is guided by the International Recommendations on Refugee Statistics (IRRS), which provide standardized guidelines for data collection and reporting. These guidelines aim to ensure that data on asylum-seekers is comparable across different countries and regions, facilitating a coherent global understanding of asylum trends. The IRRS recommends that data be disaggregated by key variables such as the stage of the asylum process, the demographic characteristics of applicants, and the country of origin.\n\n### Development relevance:\nData on asylum-seekers is vital for understanding the demand for international protection and the capacity of states to process asylum claims, aligning with the goals of the Global Compact on Refugees. This data underpins ongoing efforts by the World Bank and the Office of the United Nations High Commissioner for Refugees (UNHCR) to strengthen asylum systems through projects like the Asylum Capacity Support Program, which assists countries in enhancing their legal frameworks and operational capacities. Moreover, the data is used in international dialogues, such as the UN High-Level Meeting on Refugees and Migrants, to advocate for fair and efficient asylum procedures and ensure that human rights obligations are upheld.\n\n### Limitations and exceptions:\nVariability in national asylum procedures and processing times can lead to inconsistencies in data reporting, affecting the accuracy of cross-country comparisons. Unregistered asylum claims or informal border crossings may result in incomplete data, underestimating the true scope of the need for protection. Additionally, the changing status of individuals, such as those asylum-seekers that are recognized as refugees, complicates the tracking and categorization of data.", "origins": [ { "producer": "UN High Commissioner for Refugees (UNHCR), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download. Indicator SM.POP.ASYS.EA (https://data.worldbank.org/indicator/SM.POP.ASYS.EA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.ASYS.EA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_asys_eo": { "title": "Asylum-seekers by country or territory of origin", "description_from_producer": "Asylum-seekers are individuals who have sought international protection and whose claims for refugee status have not yet been determined. This includes those who are in various stages of the asylum process, such as initial application, appeal, or awaiting final decision. In specific contexts, asylum-seekers may also include those who are seeking protection under complementary forms of protection, and those enjoying temporary protection, but whose claims are still under consideration.\n\n### Statistical concept and methodology:\nMethodology: Data collection on asylum-seekers is primarily handled through administrative records maintained by national authorities responsible for processing asylum claims. These records capture various stages of the asylum process, including the number of new applications, repeat applications, and cases that are pending resolution.\n\n\n\nThe methodology for collecting asylum-seeker data is guided by the International Recommendations on Refugee Statistics (IRRS), which provide standardized guidelines for data collection and reporting. These guidelines aim to ensure that data on asylum-seekers is comparable across different countries and regions, facilitating a coherent global understanding of asylum trends. The IRRS recommends that data be disaggregated by key variables such as the stage of the asylum process, the demographic characteristics of applicants, and the country of origin.\n\n### Development relevance:\nData on asylum-seekers is vital for understanding the demand for international protection and the capacity of states to process asylum claims, aligning with the goals of the Global Compact on Refugees. This data underpins ongoing efforts by the World Bank and the Office of the United Nations High Commissioner for Refugees (UNHCR) to strengthen asylum systems through projects like the Asylum Capacity Support Program, which assists countries in enhancing their legal frameworks and operational capacities. Moreover, the data is used in international dialogues, such as the UN High-Level Meeting on Refugees and Migrants, to advocate for fair and efficient asylum procedures and ensure that human rights obligations are upheld.\n\n### Limitations and exceptions:\nVariability in national asylum procedures and processing times can lead to inconsistencies in data reporting, affecting the accuracy of cross-country comparisons. Unregistered asylum claims or informal border crossings may result in incomplete data, underestimating the true scope of the need for protection. Additionally, the changing status of individuals, such as those asylum-seekers that are recognized as refugees, complicates the tracking and categorization of data.", "origins": [ { "producer": "UN High Commissioner for Refugees (UNHCR), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download. Indicator SM.POP.ASYS.EO (https://data.worldbank.org/indicator/SM.POP.ASYS.EO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.ASYS.EO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_fdip": { "title": "Forcibly displaced people", "description_from_producer": "Forcibly displaced people are represented by the sum of (1) refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR), (2) refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), (3) asylum-seekers, (4) other people in need of international protection, and (5) internally displaced people (IDPs). Situations in which people are reported in more than one of these categories are accounted for, to the extent possible.\n\n### Statistical concept and methodology:\nMethodology: Data collection on forcibly displaced people combines information on five key population groups: (1) refugees (and people in a refugee-like situation) under the mandate of the United Nations High Commissioner for Refugees (UNHCR), (2) refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), (3) asylum-seekers, (4) other people in need of international protection, and (5) internally displaced people (IDPs). The primary data sources include administrative records, population censuses, and household surveys, as well as operational data from humanitarian organizations such as UNHCR, UNRWA, and the Internal Displacement Monitoring Centre (IDMC). Data is reported as stock data at specific points in time, typically the end of the calendar year, providing a snapshot of forcibly displaced populations globally. Each category follows established international methodologies to ensure consistency and comparability.\n\nAt the end of 2023 / 2024 / 2025, the UNRWA estimates that 70 per cent of the 1.7 / 2.0 / 2.0 million IDPs in the Gaza Strip at end-2023 / -2024 / -2025 were Palestine refugees under its mandate. These internally displaced refugees under the mandate of UNRWA are only counted once in the forcibly displaced total (i.e. deducted once from the total amount to avoid double counting). The forcibly displaced population category provides a comprehensive and standardized picture of displacement dynamics across different contexts and regions. Statistics for the indicator are included from 2010 onwards, as at this time data on internal displacement situations became more available and more consistent.\n\n### Development relevance:\nData on forcibly displaced people provide a comprehensive measure of global displacement, encompassing refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) and the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), asylum-seekers, other people in need of international protection, and internally displaced people (IDPs). This aggregate data is essential for designing holistic responses to forced displacement, supporting initiatives like the Global Compact on Refugees. It enables governments, humanitarian organizations, and development partners to better understand the scale of displacement and mobilize resources to address the multifaceted needs of displaced populations.\n\n### Limitations and exceptions:\nAggregating data across diverse categories introduces challenges in maintaining comparability and consistency. Variations in data collection methods and definitions\u2014such as differing criteria for refugee or IDP status\u2014can complicate efforts to standardize figures across countries and regions. Additionally, dynamic changes in displacement, including secondary movements or status transitions (e.g., from asylum-seeker to refugee), may lead to double counting or data gaps. The diverse sources of data, ranging from administrative records to field surveys, further underscore the importance of contextualizing and cautiously interpreting the aggregate figures.", "origins": [ { "producer": "UN High Commissioner for Refugees (UNHCR), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download. Indicator SM.POP.FDIP (https://data.worldbank.org/indicator/SM.POP.FDIP). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.FDIP", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_idpc": { "title": "Internally displaced people (IDP) by country or territory of asylum / origin", "description_from_producer": "Internally displaced people (IDPs) are persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of, or in order to avoid the effects of armed conflict, situations of generalized violence, and violations of human rights, and who have not crossed an internationally recognized State border.\n\n### Statistical concept and methodology:\nMethodology: Data collection on internally displaced people (IDPs) is based on a combination of population censuses, household surveys, administrative records, and operational data from humanitarian organizations. Population censuses provide a baseline count of IDPs, while household surveys are often used to collect more detailed and current data, particularly in situations where displacement is ongoing or rapidly changing.\n\n\n\nAdministrative records from local governments and humanitarian agencies provide additional data on the movements, living conditions, and access to services for IDP populations.\n\n\n\nThe Internal Displacement Monitoring Centre (IDMC) compiles and publishes IDP statistics from multiple sources. These include operational data produced by the Joint IDP Profiling Service (JIPS) and the International Organization for Migration (IOM), particularly in conflict-affected regions where official statistics may be less reliable or incomplete.\n\n\n\nThe methodology for collecting and reporting IDP data adheres to the International Recommendations on IDP Statistics (IRIS), developed by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS). The IRIS framework provides a detailed structure for defining and measuring IDP populations, ensuring that data on IDPs is consistent, reliable, and comparable across different contexts and countries. The framework includes recommendations for both stock data (the number of IDPs at a given time) and flow data (movements into and out of the IDP population), providing a comprehensive understanding of internal displacement dynamics. This methodology has been applied in various contexts, such as in the Democratic Republic of the Congo and Colombia, where detailed IDP profiling has been essential for understanding the needs and vulnerabilities of displaced populations.\n\n### Development relevance:\nData on internally displaced people (IDPs) is essential for guiding humanitarian assistance, informing national development plans, and shaping international responses to internal displacement, particularly in fragile and conflict-affected states. This data is crucial for initiatives like the World Bank\u2019s Fragility, Conflict, and Violence (FCV) Strategy, which aims to address\n\n### Limitations and exceptions:\nCollecting accurate data on internally displaced people (IDPs) is challenging in conflict zones and areas with limited access, leading to potential underreporting or incomplete coverage. Repeated displacements and the complex dynamics of internal conflicts can result in double counting or misclassification of IDPs. Additionally, differing definitions of IDPs across countries and regions can hinder the comparability and reliability of the data, complicating international efforts to address internal displacement.", "origins": [ { "producer": "UNHCR and Internal Displacement Monitoring Centre (IDMC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download;\nGlobal Internal Displacement Database, Internal Displacement Monitoring Centre (IDMC), uri: https://www.internal-displacement.org/database/displacement-data/. Indicator SM.POP.IDPC (https://data.worldbank.org/indicator/SM.POP.IDPC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.IDPC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_netm": { "title": "Net migration", "description_from_producer": "Net migration is the net total of migrants during the period, that is, the number of immigrants minus the number of emigrants, including both citizens and noncitizens.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: When there is insufficient data, net migration is derived through the difference between the overall population growth rate and the rate of natural increase (the difference between the birth rate and the death rate) during the same period. Such calculations are usually made for intercensal periods. The estimates are also derived from the data on foreign-born population - people who have residence in one country but were born in another country. When data on the foreign-born population are not available, data on foreign population - that is, people who are citizens of a country other than the country in which they reside - are used as estimates.\nStatistical concept(s): The United Nations Population Division provides data on net migration and migrant stock. Because data on migrant stock is difficult for countries to collect, the United Nations Population Division takes into account the past migration history of a country or area, the migration policy of a country, and the influx of refugees in recent periods when deriving estimates of net migration. The data to calculate these estimates come from a variety of sources, including border statistics, administrative records, surveys, and censuses.\n\n### Development relevance:\nMovement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\n\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers.\n\n\n\nBut refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom.\n\n### Limitations and exceptions:\nInternational migration is the component of population change most difficult to measure and estimate reliably. Thus, the quality and quantity of the data used in the estimation and projection of net migration varies considerably by country. Furthermore, the movement of people across international boundaries, which is very often a response to changing socio-economic, political and environmental forces, is subject to a great deal of volatility. Refugee movements, for instance, may involve large numbers of people moving across boundaries in a short time. For these reasons, projections of future international migration levels are the least robust part of current population projections and reflect mainly a continuation of recent levels and trends in net migration.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SM.POP.NETM (https://data.worldbank.org/indicator/SM.POP.NETM). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.NETM", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_opip_ea": { "title": "Other people in need of international protection by country or territory of asylum", "description_from_producer": "Other people in need of international protection refer to people who are outside their country or territory of origin, typically because they have been forcibly displaced across international borders, who have not been reported under other categories (including asylum-seekers and refugees) but who likely need international protection, including protection against forced return, as well as access to basic services on a temporary or longer-term basis.\n\n### Statistical concept and methodology:\nMethodology: Data collection on other people in need of international protection involves methodologies similar to those used for refugees and asylum-seekers.\n\n\n\nData is primarily collected through administrative records, which track the number of individuals identified as needing international protection but who do not fall under the traditional definitions of refugees or asylum-seekers. These records are often supplemented by data from humanitarian operations and targeted surveys conducted in areas experiencing large-scale displacement.\n\n\n\nSince 2018, the Office of the United Nations High Commissioner for Refugees (UNHCR) has expanded its statistical frameworks to include this category, reflecting the growing recognition of the diverse forms of displacement and protection needs that exist beyond the traditional categories. The methodology for collecting and reporting data on these populations ensures that they are accurately represented in global statistics, and are comprehensive and comparable across different regions and timeframes, supporting more targeted and effective protection responses.\n\n### Development relevance:\nData on other people in need of international protection is essential for identifying and responding to the needs of individuals who fall outside traditional refugee definitions but still require protection and assistance. This data is crucial for initiatives like the Office of the United Nations High Commissioner for Refugees\u2019 (UNHCR) broader protection strategies and supports the World Bank\u2019s work on mixed migration flows, where displaced populations and migrants are addressed through integrated approaches. The data also plays a key role in informing policy discussions at international forums, such as the UN General Assembly, where member states explore comprehensive protection frameworks for vulnerable groups.\n\n### Limitations and exceptions:\nThe definitions of people included in this category can be inconsistent across countries in the data that is collected and reported, affecting its comparability across contexts.", "origins": [ { "producer": "UN High Commissioner for Refugees (UNHCR), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download. Indicator SM.POP.OPIP.EA (https://data.worldbank.org/indicator/SM.POP.OPIP.EA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.OPIP.EA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_opip_eo": { "title": "Other people in need of international protection by country or territory of origin", "description_from_producer": "Other people in need of international protection refer to people who are outside their country or territory of origin, typically because they have been forcibly displaced across international borders, who have not been reported under other categories (including asylum-seekers and refugees) but who likely need international protection, including protection against forced return, as well as access to basic services on a temporary or longer-term basis.\n\n### Statistical concept and methodology:\nMethodology: Data collection on other people in need of international protection involves methodologies similar to those used for refugees and asylum-seekers.\n\n\n\nData is primarily collected through administrative records, which track the number of individuals identified as needing international protection but who do not fall under the traditional definitions of refugees or asylum-seekers. These records are often supplemented by data from humanitarian operations and targeted surveys conducted in areas experiencing large-scale displacement.\n\n\n\nSince 2018, the Office of the United Nations High Commissioner for Refugees (UNHCR) has expanded its statistical frameworks to include this category, reflecting the growing recognition of the diverse forms of displacement and protection needs that exist beyond the traditional categories. The methodology for collecting and reporting data on these populations ensures that they are accurately represented in global statistics, and are comprehensive and comparable across different regions and timeframes, supporting more targeted and effective protection responses.\n\n### Development relevance:\nData on other people in need of international protection is essential for identifying and responding to the needs of individuals who fall outside traditional refugee definitions but still require protection and assistance. This data is crucial for initiatives like the Office of the United Nations High Commissioner for Refugees\u2019 (UNHCR) broader protection strategies and supports the World Bank\u2019s work on mixed migration flows, where displaced populations and migrants are addressed through integrated approaches. The data also plays a key role in informing policy discussions at international forums, such as the UN General Assembly, where member states explore comprehensive protection frameworks for vulnerable groups.\n\n### Limitations and exceptions:\nThe definitions of people included in this category can be inconsistent across countries in the data that is collected and reported, affecting its comparability across contexts.", "origins": [ { "producer": "UN High Commissioner for Refugees (UNHCR), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download. Indicator SM.POP.OPIP.EO (https://data.worldbank.org/indicator/SM.POP.OPIP.EO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.OPIP.EO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_rhcr_ea": { "title": "Refugees under the mandate of the UNHCR by country or territory of asylum", "description_from_producer": "Refugees under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) include individuals recognized under the 1951 Convention relating to the Status of Refugees, its 1967 Protocol, the 1969 Organization of African Unity (OAU) Convention Governing the Specific Aspects of Refugee Problems in Africa, the refugee definition contained in the 1984 Cartagena Declaration on Refugees as incorporated into national laws, those recognized in accordance with the UNHCR Statute, individuals granted complementary forms of protection, and those enjoying temporary protection. The refugee population also includes people in a refugee-like situation, which is a category that is descriptive in nature and includes groups of people who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained. Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the UNHCR.\n\n### Statistical concept and methodology:\nMethodology: Data collection on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is based on a coordinated effort between national governments and UNHCR.\n\n\n\nThe primary sources of data include administrative records and registration systems maintained by immigration or asylum agencies, including UNHCR itself, which provide detailed demographic information, such as age, sex, and nationality. These records are generally reported as stock data, reflecting the number of refugees at a specific point in time, typically at the end of the calendar year.\n\n\n\nThe collection process follows the guidelines outlined in the International Recommendations on Refugee Statistics (IRRS), developed by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS). The IRRS helps to standardize data collection methods across different countries. The IRRS emphasizes the importance of disaggregating data by key demographic variables and geographical locations, facilitating a more granular analysis of refugee populations.\n\n### Development relevance:\nData on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is crucial for informing global and national policies that address the immediate and long-term needs of displaced populations. This data supports ongoing initiatives such as the World Bank\u2019s Global Concessional Financing Facility, which provides financial resources to middle-income countries hosting large numbers of refugees, thereby fostering resilience and stability. Additionally, the data is instrumental for discussions in international forums like the Global Refugee Forum and the UNHCR Executive Committee, where stakeholders assess the effectiveness of refugee response strategies and mobilize support for burden-sharing among nations.\n\n### Limitations and exceptions:\nInconsistencies in data collection standards across countries can complicate the comparability of refugee data globally, making it challenging to create uniform policies. The fluidity of refugee movements and states\u2019 processing of status changes, such as shifts from asylum-seeker to refugee, can impact the timeliness of the data. Furthermore, political pressures may influence how governments report refugee figures, potentially leading to underreporting or misrepresentation of the situation.", "origins": [ { "producer": "UN High Commissioner for Refugees (UNHCR), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download. Indicator SM.POP.RHCR.EA (https://data.worldbank.org/indicator/SM.POP.RHCR.EA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.RHCR.EA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_rhcr_eo": { "title": "Refugees under the mandate of the UNHCR by country or territory of origin", "description_from_producer": "Refugees under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) include individuals recognized under the 1951 Convention relating to the Status of Refugees, its 1967 Protocol, the 1969 Organization of African Unity (OAU) Convention Governing the Specific Aspects of Refugee Problems in Africa, the refugee definition contained in the 1984 Cartagena Declaration on Refugees as incorporated into national laws, those recognized in accordance with the UNHCR Statute, individuals granted complementary forms of protection, and those enjoying temporary protection. The refugee population also includes people in a refugee-like situation, which is a category that is descriptive in nature and includes groups of people who are outside their country or territory of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained. Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the UNHCR.\n\n### Statistical concept and methodology:\nMethodology: Data collection on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is based on a coordinated effort between national governments and UNHCR.\n\n\n\nThe primary sources of data include administrative records and registration systems maintained by immigration or asylum agencies, including UNHCR itself, which provide detailed demographic information, such as age, sex, and nationality. These records are generally reported as stock data, reflecting the number of refugees at a specific point in time, typically at the end of the calendar year.\n\n\n\nThe collection process follows the guidelines outlined in the International Recommendations on Refugee Statistics (IRRS), developed by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS). The IRRS helps to standardize data collection methods across different countries. The IRRS emphasizes the importance of disaggregating data by key demographic variables and geographical locations, facilitating a more granular analysis of refugee populations.\n\n### Development relevance:\nData on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR) is crucial for informing global and national policies that address the immediate and long-term needs of displaced populations. This data supports ongoing initiatives such as the World Bank\u2019s Global Concessional Financing Facility, which provides financial resources to middle-income countries hosting large numbers of refugees, thereby fostering resilience and stability. Additionally, the data is instrumental for discussions in international forums like the Global Refugee Forum and the UNHCR Executive Committee, where stakeholders assess the effectiveness of refugee response strategies and mobilize support for burden-sharing among nations.\n\n### Limitations and exceptions:\nInconsistencies in data collection standards across countries can complicate the comparability of refugee data globally, making it challenging to create uniform policies. The fluidity of refugee movements and states\u2019 processing of status changes, such as shifts from asylum-seeker to refugee, can impact the timeliness of the data. Furthermore, political pressures may influence how governments report refugee figures, potentially leading to underreporting or misrepresentation of the situation.", "origins": [ { "producer": "UN High Commissioner for Refugees (UNHCR), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download. Indicator SM.POP.RHCR.EO (https://data.worldbank.org/indicator/SM.POP.RHCR.EO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.RHCR.EO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_rrwa_ea": { "title": "Refugees under the mandate of the UNRWA by country or territory of asylum", "description_from_producer": "Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948 and who lost both home and means of livelihood as a result of the 1948 conflict. Palestine Refugees, and descendants of Palestine refugee males, including legally adopted children, are eligible to register for UNRWA services. UNRWA accepts new applications from persons who wish to be registered as Palestine Refugees. Once they are registered with UNRWA, persons in this category are referred to as Registered Refugees. Refugees under the mandate of the UNRWA are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR).\n\n### Statistical concept and methodology:\nMethodology: Data collection on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is primarily based on administrative records maintained by the UNRWA in line with UNRWA\u2019s internal guidelines and operational frameworks.\n\n\n\nUNRWA's registration system is a key source, capturing detailed information on individuals registered as Palestine refugees, including their demographic characteristics as well as the country or territory of asylum, to facilitate a nuanced understanding of the refugee population's distribution and characteristics.\n\n\n\nIn addition to administrative records, UNRWA also collaborates with host countries and other international organizations to enrich its data, particularly in contexts where operational challenges might limit direct data collection.\n\n### Development relevance:\nData on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is critical for addressing the unique and ongoing displacement situation of Palestinian refugees. This data informs humanitarian and development strategies in associated key regions, including the Gaza Strip, and West Bank. It supports international initiatives, particularly in areas related to health, education, and poverty reduction. Additionally, the data plays a significant role in global policy discussions to ensure adequate resources for sustaining vital services for Palestinian refugees.\n\n### Limitations and exceptions:\nThe long-standing nature of Palestinian refugee status creates challenges in distinguishing generational shifts and new displacements. Data collection is further hindered by the varying legal and socio-political conditions in host countries, impacting the accuracy and comparability of reported figures. Additionally, the absence of a durable solution for Palestinian refugees exacerbates data fluidity, with changes in status, location, and service eligibility complicating longitudinal tracking. These factors underline the need for cautious interpretation and contextual understanding of data on Palestinian refugees.", "origins": [ { "producer": "UNHCR and UNRWA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download;\nStatistics Bulletin, UN Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), uri: https://www.unrwa.org/what-we-do/unrwa-statistics-bulletin. Indicator SM.POP.RRWA.EA (https://data.worldbank.org/indicator/SM.POP.RRWA.EA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.RRWA.EA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_rrwa_eo": { "title": "Refugees under the mandate of the UNRWA by country or territory of origin", "description_from_producer": "Refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), Palestine Refugees, are persons whose normal place of residence was Palestine during the period 1 June 1946 to 15 May 1948 and who lost both home and means of livelihood as a result of the 1948 conflict. Palestine Refugees, and descendants of Palestine refugee males, including legally adopted children, are eligible to register for UNRWA services. UNRWA accepts new applications from persons who wish to be registered as Palestine Refugees. Once they are registered with UNRWA, persons in this category are referred to as Registered Refugees. Refugees under the mandate of the UNRWA are not typically included in the statistics on refugees (and people in a refugee-like situation) under the mandate of the Office of the United Nations High Commissioner for Refugees (UNHCR).\n\n### Statistical concept and methodology:\nMethodology: Data collection on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is primarily based on administrative records maintained by the UNRWA in line with UNRWA\u2019s internal guidelines and operational frameworks.\n\n\n\nUNRWA's registration system is a key source, capturing detailed information on individuals registered as Palestine refugees, including their demographic characteristics as well as the country or territory of asylum, to facilitate a nuanced understanding of the refugee population's distribution and characteristics.\n\n\n\nIn addition to administrative records, UNRWA also collaborates with host countries and other international organizations to enrich its data, particularly in contexts where operational challenges might limit direct data collection.\n\n### Development relevance:\nData on refugees under the mandate of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) is critical for addressing the unique and ongoing displacement situation of Palestinian refugees. This data informs humanitarian and development strategies in associated key regions, including the Gaza Strip, and West Bank. It supports international initiatives, particularly in areas related to health, education, and poverty reduction. Additionally, the data plays a significant role in global policy discussions to ensure adequate resources for sustaining vital services for Palestinian refugees.\n\n### Limitations and exceptions:\nThe long-standing nature of Palestinian refugee status creates challenges in distinguishing generational shifts and new displacements. Data collection is further hindered by the varying legal and socio-political conditions in host countries, impacting the accuracy and comparability of reported figures. Additionally, the absence of a durable solution for Palestinian refugees exacerbates data fluidity, with changes in status, location, and service eligibility complicating longitudinal tracking. These factors underline the need for cautious interpretation and contextual understanding of data on Palestinian refugees.", "origins": [ { "producer": "UNHCR and UNRWA, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Refugee Population Statistics Database, UN High Commissioner for Refugees (UNHCR), uri: https://www.unhcr.org/refugee-statistics/download;\nStatistics Bulletin, UN Relief and Works Agency for Palestine Refugees in the Near East (UNRWA), uri: https://www.unrwa.org/what-we-do/unrwa-statistics-bulletin. Indicator SM.POP.RRWA.EO (https://data.worldbank.org/indicator/SM.POP.RRWA.EO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.RRWA.EO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_totl": { "title": "International migrant stock, total", "description_from_producer": "International migrant stock, total is the number of people at mid-year born in a country other than that in which they live. It also includes refugees.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The basic data to estimate the international migrant stock were obtained mostly from population censuses held during the decennial rounds of censuses. Some of the data used were obtained from population registers and nationally representative surveys.\n\nIn the majority of cases, the sources available had gathered information on the place of birth of the enumerated population, thus allowing for the identification of the foreign-born population. In estimating the international migrant stock, international migrants have been equated with the foreign-born whenever possible. In most countries lacking data on place of birth, information on the country of citizenship of those enumerated was available and was used as the basis for the identification of international migrants, thus effectively equating international migrants with foreign citizens.\n\nFor countries or areas for which no information was available on the international migrant stock, the estimates were imputed.\n\n### Development relevance:\nMovement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom.\n\n### Limitations and exceptions:\nIn deriving the estimates, an international migrant was equated to a person living in a country other than that in which he or she was born. That is, the number of international migrants, also called the international migrant stock, would represent the number of foreign-born persons enumerated in the countries or areas constituting the world. However, because several countries lack data on the foreign-born, data on the number of foreigners, if available, were used instead as the basis of estimation. Consequently, the overall number of migrants in world regions or at the global level do not quite represent the overall number of foreign-born persons.\n\nThe disintegration and reunification of countries causes discontinuities in the change of the international migrant stock. Because an international migrant is equated with a person who was born outside the country in which he or she resides, when a country disintegrates, persons who had been internal migrants because they had moved from one part of the country to another may become, overnight, international migrants without having moved at that time. Such changes introduce artificial but unavoidable discontinuities in the trend of the international migrant stock. The reunification of States also introduces discontinuities, but in the opposite direction.\n\nWorld aggregates are computed by the World Bank and include economies covered by the World Development Indicators. Therefore, the world total figures or world averages may differ from those published by the United Nations Population Division (UNPD).\n\nThe proportion of international migrant stock (SM.POP.TOTL.ZS) is calculated by the UNPD using UNPD\u2019s total population data, which may differ from the total population data in the World Development Indicators (WDI). Consequently, the number of international migrant stock (SM.POP.TOTL) may not match the result obtained by multiplying the total population in the WDI by the proportion of international migrant stock (SM.POP.TOTL.ZS).", "origins": [ { "producer": "International Migrant Stock, United Nations, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Migrant Stock, United Nations (UN), publisher: UN Population Division. Indicator SM.POP.TOTL (https://data.worldbank.org/indicator/SM.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sm_pop_totl_zs": { "title": "International migrant stock (% of population)", "description_from_producer": "International migrant stock (% of population) is the proportion of people at mid-year born in a country other than that in which they live. It also includes refugees.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The basic data to estimate the international migrant stock were obtained mostly from population censuses held during the decennial rounds of censuses. Some of the data used were obtained from population registers and nationally representative surveys.\n\nIn the majority of cases, the sources available had gathered information on the place of birth of the enumerated population, thus allowing for the identification of the foreign-born population. In estimating the international migrant stock, international migrants have been equated with the foreign-born whenever possible. In most countries lacking data on place of birth, information on the country of citizenship of those enumerated was available and was used as the basis for the identification of international migrants, thus effectively equating international migrants with foreign citizens.\n\nFor countries or areas for which no information was available on the international migrant stock, the estimates were imputed.\n\n### Development relevance:\nMovement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. In most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom.\n\n### Limitations and exceptions:\nIn deriving the estimates, an international migrant was equated to a person living in a country other than that in which he or she was born. That is, the number of international migrants, also called the international migrant stock, would represent the number of foreign-born persons enumerated in the countries or areas constituting the world. However, because several countries lack data on the foreign-born, data on the number of foreigners, if available, were used instead as the basis of estimation. Consequently, the overall number of migrants in world regions or at the global level do not quite represent the overall number of foreign-born persons.\n\nThe disintegration and reunification of countries causes discontinuities in the change of the international migrant stock. Because an international migrant is equated with a person who was born outside the country in which he or she resides, when a country disintegrates, persons who had been internal migrants because they had moved from one part of the country to another may become, overnight, international migrants without having moved at that time. Such changes introduce artificial but unavoidable discontinuities in the trend of the international migrant stock. The reunification of States also introduces discontinuities, but in the opposite direction.\n\nWorld aggregates are computed by the World Bank and include economies covered by the World Development Indicators. Therefore, the world total figures or world averages may differ from those published by the United Nations Population Division (UNPD).\n\nThe proportion of international migrant stock (SM.POP.TOTL.ZS) is calculated by the UNPD using UNPD\u2019s total population data, which may differ from the total population data in the World Development Indicators (WDI). Consequently, the number of international migrant stock (SM.POP.TOTL) may not match the result obtained by multiplying the total population in the WDI by the proportion of international migrant stock (SM.POP.TOTL.ZS).", "origins": [ { "producer": "International Migrant Stock, United Nations, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "International Migrant Stock, United Nations (UN), publisher: UN Population Division. Indicator SM.POP.TOTL.ZS (https://data.worldbank.org/indicator/SM.POP.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SM.POP.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "sn_itk_defc_zs": { "title": "Prevalence of undernourishment (% of population)", "description_short": "Share of individuals that have a daily food intake that is insufficient to provide the amount of dietary energy required to maintain a normal, active, and healthy life.", "description_from_producer": "Prevalence of undernourishments is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. Data showing as 2.5 may signify a prevalence of undernourishment below 2.5%.\n\nLimitations and exceptions: From a policy and program standpoint, this measure has its limits. First, food insecurity exists even where food availability is not a problem because of inadequate access of poor households to food. Second, food insecurity is an individual or household phenomenon, and the average food available to each person, even corrected for possible effects of low income, is not a good predictor of food insecurity among the population. And third, nutrition security is determined not only by food security but also by the quality of care of mothers and children and the quality of the household's health environment (Smith and Haddad 2000).\n\nStatistical concept and methodology: Data on undernourishment are from the Food and Agriculture Organization (FAO) of the United Nations and measure food deprivation based on average food available for human consumption per person, the level of inequality in access to food, and the minimum calories required for an average person.", "description_key": "Countries and regions with rates below 2.5% are coded as \"2.5%\" in the data.", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations (FAO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Food and Agriculture Organization of the United Nations (FAO), uri: http://www.fao.org/faostat/en/#home. Indicator SN.ITK.DEFC.ZS (https://data.worldbank.org/indicator/SN.ITK.DEFC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SN.ITK.DEFC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "numDecimalPlaces": 1, "shortUnit": "%", "unit": "% of population" }, "processing_level": "minor", "presentation": { "title_public": "Share of the population that is undernourished", "attribution_short": "World Bank", "attribution": "Food and Agriculture Organization of the United Nations (via World Bank)", "topic_tags": [ "Hunger & Undernourishment" ] } }, "sn_itk_msfi_zs": { "title": "Prevalence of moderate or severe food insecurity in the population (%)", "description_from_producer": "The percentage of people in the population who live in households classified as moderately or severely food insecure. A household is classified as moderately or severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to low quality diets and might have been forced to also reduce the quantity of food they would normally eat because of a lack of money or other resources.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: The assessment is conducted using data collected with the Food Insecurity Experience Scale or a compatible experience-based food security measurement questionnaire (such as the HFSSM). The probability to be food insecure is estimated using the one-parameter logistic Item Response Theory model (the Rasch model) and thresholds for classification are made cross country comparable by calibrating the metrics obtained in each country against the FIES global reference scale, maintained by FAO. The threshold to classify \"moderate or severe\" food insecurity corresponds to the severity associated with the item \"having to eat less\" on the global FIES scale. It is an indicator of lack of food access.The indicator is calculated as an average over 3 years (eg. data for 2015 is the average of 2014-2016 data).\n\n### Development relevance:\nFood insecurity at moderate levels of severity is typically associated with the inability to regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at moderate levels can be considered a predictor of various forms of diet-related health conditions in the population, associated with micronutrient deficiency and unbalanced diets. Severe levels of food insecurity, on the other hand, imply a high probability of reduced food intake and therefore can lead to more severe forms of undernutrition, including hunger. FAO has identified the FIES as the tool with the greatest potential for becoming a global standard capable of providing comparable information on food insecurity experience across countries and population groups to track progress on reducing food insecurity and\n\nhunger", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Food and Agriculture Organization of the United Nations (FAO). Indicator SN.ITK.MSFI.ZS (https://data.worldbank.org/indicator/SN.ITK.MSFI.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SN.ITK.MSFI.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sn_itk_salt_zs": { "title": "Consumption of iodized salt (% of households)", "description_from_producer": "Percentage of households which have salt they used for cooking that tested positive (>0ppm) for presence of iodine.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Most of the data on consumption of iodized salt are derived from household surveys. For the data that are from household surveys, the year refers to the survey year.\n\n### Development relevance:\nIodine deficiency can lead to a variety of health and developmental consequences known as iodine deficiency disorders (IDDs). Iodine deficiency is a major cause of preventable mental retardation. It is especially damaging during pregnancy and in early childhood. In their most severe forms, IDDs can lead to cretinism, stillbirth and miscarriage; even mild deficiency can cause a significant loss of learning ability. Thus, it is crucially important that pregnant women and young children in particular get adequate levels of iodine.\n\nIDD can easily be prevented at low cost, however, with small quantities of iodine. One of the best and least expensive methods of preventing iodine deficiency disorder is by simply iodizing table salt, which is currently done in many countries. It represents one of the easiest and most cost-effective interventions for social and economic development.\n\n### Other notes:\nIodine deficiency is the single most important cause of preventable mental retardation, contributes significantly to the risk of stillbirth and miscarriage, and increases the incidence of infant mortality. A diet low in iodine is the main cause of iodine deficiency. It usually occurs among populations living in areas where the soil has been depleted of iodine. If soil is deficient in iodine, then so are the plants grown in it, including the grains and vegetables that people and animals consume. There are almost no countries in the world where iodine deficiency has not been a public health problem. Many newborns in low- and middle-income countries remain unprotected from the lifelong consequences of brain damage associated with iodine deficiency disorders, which affect a child's ability to learn and to earn a living as an adult, and in turn prevents children, communities, and countries from fulfilling their potential (UNICEF, www.childinfo.org). Widely used and inexpensive, iodized salt is the best source of iodine, and a global campaign to iodize edible salt is significantly reducing the risks associated with iodine deficiency.", "origins": [ { "producer": "UNICEF Global Databases on Iodized Salt, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF Global Databases on Iodized salt, UN Children's Fund (UNICEF), publisher: Division of Data, Analysis, Planning and Monitoring. Indicator SN.ITK.SALT.ZS (https://data.worldbank.org/indicator/SN.ITK.SALT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SN.ITK.SALT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of households", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of households" } }, "sn_itk_svfi_zs": { "title": "Prevalence of severe food insecurity in the population (%)", "description_from_producer": "The percentage of people in the population who live in households classified as severely food insecure. A household is classified as severely food insecure when at least one adult in the household has reported to have been exposed, at times during the year, to several of the most severe experiences described in the FIES questions, such as to have been forced to reduce the quantity of the food, to have skipped meals, having gone hungry, or having to go for a whole day without eating because of a lack of money or other resources.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: The assessment is conducted using data collected with the Food Insecurity Experience Scale or a compatible experience-based food security measurement questionnaire (such as the HFSSM). The probability to be food insecure is estimated using the one-parameter logistic Item Response Theory model (the Rasch model) and thresholds for classification are made cross country comparable by calibrating the metrics obtained in each country against the FIES global reference scale, maintained by FAO. The threshold to classify \"severe\" food insecurity corresponds to the severity associated with the item \"having not eaten for an entire day\" on the global FIES scale. It is an indicator of lack of food access.The indicator is calculated as an average over 3 years (eg. data for 2015 is the average of 2014-2016 data).\n\n### Development relevance:\nFood insecurity at moderate levels of severity is typically associated with the inability to regularly eat healthy, balanced diets. As such, high prevalence of food insecurity at moderate levels can be considered a predictor of various forms of diet-related health conditions in the population, associated with micronutrient deficiency and unbalanced diets. Severe levels of food insecurity, on the other hand, imply a high probability of reduced food intake and therefore can lead to more severe forms of undernutrition, including hunger. FAO has identified the FIES as the tool with the greatest potential for becoming a global standard capable of providing comparable information on food insecurity experience across countries and population groups to track progress on reducing food insecurity and\n\nhunger", "origins": [ { "producer": "Food and Agriculture Organization of the United Nations and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Food and Agriculture Organization of the United Nations (FAO). Indicator SN.ITK.SVFI.ZS (https://data.worldbank.org/indicator/SN.ITK.SVFI.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SN.ITK.SVFI.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sn_itk_vita_zs": { "title": "Vitamin A supplementation coverage rate (% of children ages 6-59 months)", "description_from_producer": "Vitamin A supplementation coverage rate refers to the percentage of children ages 6-59 months old receiving two high-dose vitamin A supplements in a calendar year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Household Surveys including DHS, MICS and other national surveys\nStatistical concept(s): The World Health Organization has classified vitamin A deficiency as a public health problem affecting many children ages 6-59 months, with the highest rates in sub-Saharan Africa and South Asia.\n\n### Development relevance:\nVitamin A deficiency is the leading cause of preventable childhood blindness and increases the risk of death from common childhood illnesses such as diarrhoea. Periodic, high-dose vitamin A supplementation is a proven, low-cost intervention which has been shown to reduce all-cause mortality.\n\n### Other notes:\nVitamin A is essential for optimal functioning of the immune system. Vitamin A deficiency, a leading cause of blindness, also causes a greater risk of dying from a range of childhood ailments such as measles, malaria, and diarrhea. In low- and middle-income countries, where vitamin A is consumed largely in fruits and vegetables, daily per capita intake is often insufficient to meet dietary requirements. Providing young children with two high-dose vitamin A capsules a year is a safe, cost-effective, efficient strategy for eliminating vitamin A deficiency and improving child survival. Giving vitamin A to new breastfeeding mothers helps protect their children during the first few months of life. Food fortification with vitamin A is being introduced in many developing countries.", "origins": [ { "producer": "UNICEF Global Databases, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF Global Databases, UN Children's Fund (UNICEF), uri: https://data.unicef.org/topic/nutrition/vitamin-a-deficiency/, note: based on administrative reports from countries. Indicator SN.ITK.VITA.ZS (https://data.worldbank.org/indicator/SN.ITK.VITA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SN.ITK.VITA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of children ages 6-59 months", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of children ages 6-59 months" } }, "sp_ado_tfrt": { "title": "Adolescent fertility rate (births per 1,000 women ages 15-19)", "description_from_producer": "Adolescent fertility rate is the number of births per 1,000 women ages 15-19.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Adolescent fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without vital registration systems fertility rates are generally based on censuses or surveys.\nStatistical concept(s): Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n### Development relevance:\nAdolescent childbearing is associated with a wide range of risks for young mothers. Women who become pregnant and give birth very early in their lives as well as their newborns are subject to elevated health risks.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 3.7.2 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.ADO.TFRT (https://data.worldbank.org/indicator/SP.ADO.TFRT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.ADO.TFRT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "births per 1,000 women ages 15-19", "short_unit": "births", "display": { "shortUnit": "births", "unit": "births per 1,000 women ages 15-19" } }, "sp_dyn_amrt_fe": { "title": "Mortality rate, adult, female (per 1,000 female adults)", "description_from_producer": "Adult mortality rate, female, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old female dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated using number of survivors, l(x), at exact age x in a female period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nData from United Nations Population Division's World Populaton Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data.", "origins": [ { "producer": "UN Population Division (World Population Prospects) and Human Mortality Database, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research, uri: www.mortality.org;\nUniversity of California, Berkeley, uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies (INED), uri: www.mortality.org, note: Human Mortality Database. Indicator SP.DYN.AMRT.FE (https://data.worldbank.org/indicator/SP.DYN.AMRT.FE). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.AMRT.FE", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 female adults", "display": { "unit": "per 1,000 female adults" } }, "sp_dyn_amrt_ma": { "title": "Mortality rate, adult, male (per 1,000 male adults)", "description_from_producer": "Adult mortality rate, male, is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old male dying before reaching age 60, if subject to age-specific mortality rates of the specified year between those ages.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated using number of survivors, l(x), at exact age x in a male period life table. The formula is: (l(60)-l(15))/(l(15))*1000.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data. Where reliable age-specific mortality data are available, life tables can be constructed from age-specific mortality data, and adult mortality rates can be calculated from life tables.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nData from United Nations Population Division's World Population Prospects are originally 5-year period data and the presented are linearly interpolated by the World Bank for annual series. Therefore they may not reflect real events as much as observed data.", "origins": [ { "producer": "UN Population Division (World Population Prospects) and Human Mortality Database, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nHuman Mortality Database, Max Planck Institute for Demographic Research (MPIDR), uri: www.mortality.org;\nUniversity of California, Berkeley (USA), uri: www.mortality.org, note: Human Mortality Database;\nFrench Institute for Demographic Studies (INED), uri: www.mortality.org, note: Human Mortality Database. Indicator SP.DYN.AMRT.MA (https://data.worldbank.org/indicator/SP.DYN.AMRT.MA). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.AMRT.MA", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 male adults", "display": { "unit": "per 1,000 male adults" } }, "sp_dyn_cbrt_in": { "title": "Birth rate, crude (per 1,000 people)", "description_from_producer": "Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The crude birth rate is calculated as the number of births in a given period divided by the average population in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude birth rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 births per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division.\n\n### Development relevance:\nThe crude birth rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. When the crude death rate is subtracted from the crude birth rate, the result is the rate of natural increase, which is the rate of population change in the absence of migration.\n\n### Limitations and exceptions:\nVital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors.", "origins": [ { "producer": "United Nations Population Division, national statistical offices, Eurostat, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs);\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division. Indicator SP.DYN.CBRT.IN (https://data.worldbank.org/indicator/SP.DYN.CBRT.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.CBRT.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 people", "display": { "unit": "per 1,000 people" } }, "sp_dyn_cdrt_in": { "title": "Death rate, crude (per 1,000 people)", "description_from_producer": "Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The crude death rate is calculated as the number of deaths in a given period divided by the population exposed to risk of death in that period. For human populations the period is usually one year and, if the population changes in size over the year, the divisor is taken as the population at the mid-year. The rate is usually expressed in terms of 1,000 people: for example, a crude death rate of 9.5 (per 1000 people) in a population of 1 million would imply 9500 deaths per year in the entire population.\nStatistical concept(s): Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration. Vital rates are based on data from birth and death registration systems, censuses, and sample surveys by national statistical offices and other organizations, or on demographic analysis. Data for the most recent year for some high-income countries are provisional estimates based on vital registers. The estimates for many other countries are from the United Nations Population Division.\n\n### Development relevance:\nThe crude death rate is a good indicator of the general health status of a geographic area or population. The crude death rate is not appropriate for comparison of different populations or areas with large differences in age-distributions. Higher crude death rates can be found in some developed countries, despite high life expectancy, because typically these countries have a much higher proportion of older people, due to lower recent birth rates and lower age-specific mortality rates.\n\n### Limitations and exceptions:\nVital registers are the preferred source for these data, but in many developing countries systems for registering births and deaths are absent or incomplete because of deficiencies in the coverage of events or geographic areas. Many developing countries carry out special household surveys that ask respondents about recent births and deaths. Estimates derived in this way are subject to sampling errors and recall errors.", "origins": [ { "producer": "United Nations Population Division, national statistical offices, Eurostat, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs);\nDemographic Statistics, Eurostat (ESTAT);\nPopulation and Vital Statistics Report (various years), United Nations (UN), publisher: UN Statistical Division. Indicator SP.DYN.CDRT.IN (https://data.worldbank.org/indicator/SP.DYN.CDRT.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.CDRT.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 people", "display": { "unit": "per 1,000 people" } }, "sp_dyn_conm_zs": { "title": "Contraceptive prevalence, any modern method (% of married women ages 15-49)", "description_from_producer": "Contraceptive prevalence, any modern method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, at least one modern method of contraception. Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys.\n\n\n\nMarried women refer to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or \u201cliving together\u201d).\n\n### Development relevance:\nContraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning \u2013 whether, when, and how many children they would have.\n\n\n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted.\n\n\n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV.\n\n### Limitations and exceptions:\nWhile the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women.\n\n\n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by \u201ccurrently using\u201d a method of contraception.", "origins": [ { "producer": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and UN Population Division, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division. Indicator SP.DYN.CONM.ZS (https://data.worldbank.org/indicator/SP.DYN.CONM.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.CONM.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of women ages 15-49", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of women ages 15-49" } }, "sp_dyn_conu_zs": { "title": "Contraceptive prevalence, any method (% of married women ages 15-49)", "description_from_producer": "Contraceptive prevalence, any method is the percentage of married women ages 15-49 who are practicing, or whose sexual partners are practicing, any method of contraception (modern or traditional). Modern methods of contraception include female and male sterilization, oral hormonal pills, the intra-uterine device (IUD), the male condom, injectables, the implant (including Norplant), vaginal barrier methods, the female condom and emergency contraception. Traditional methods of contraception include rhythm (e.g., fertility awareness based methods, periodic abstinence), withdrawal and other traditional methods.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Contraceptive prevalence rates are obtained mainly from nationally representative household surveys, including: Demographic and Health Surveys; Multiple Indicator Cluster Surveys; Contraceptive Prevalence Surveys; Gender and Generations Survey; Reproductive Health Surveys; and World Fertility Surveys. Additional information was provided by other international survey programs and national surveys.\n\n\n\nMarried women refer to women who are married (defined in relation to the marriage laws or customs of a country) and to women in a union, which refers to women living with their partner in the same household (also referred to as cohabiting unions, consensual unions, unmarried unions, or \u201cliving together\u201d).\n\n### Development relevance:\nContraceptive prevalence among women of reproductive age is related to maternal and child health, as well as gender equality and HIV/AIDS. Contraceptives enable women and men to make informed decisions on family planning \u2013 whether, when, and how many children they would have.\n\n\n\nPreventing unwanted pregnancies is essential to reducing maternal deaths, especially in low- and middle- income countries where maternal mortality rate is high. With effective contraception, life-threatening pregnancy complications can be reduced, and thus maternal deaths can be averted.\n\n\n\nUsing condoms (one of the modern contraceptive methods) can prevent pregnancy as well as sexually transmitted diseases, including HIV.\n\n### Limitations and exceptions:\nWhile the data availability on contraceptive use has increased, in many countries the contraceptive use data are available only for married women.\n\n\n\nThe time frame used to assess contraceptive prevalence may vary. In many surveys, it is left to the respondent to determine what is meant by \u201ccurrently using\u201d a method of contraception.", "origins": [ { "producer": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and UN Population Division, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division. Indicator SP.DYN.CONU.ZS (https://data.worldbank.org/indicator/SP.DYN.CONU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.CONU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of women ages 15-49", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of women ages 15-49" } }, "sp_dyn_imrt_fe_in": { "title": "Mortality rate, infant, female (per 1,000 live births)", "description_from_producer": "Infant mortality rate, female is the number of female infants dying before reaching one year of age, per 1,000 female live births in a given year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nGiven that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SP.DYN.IMRT.FE.IN (https://data.worldbank.org/indicator/SP.DYN.IMRT.FE.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.IMRT.FE.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 live births", "display": { "unit": "per 1,000 live births" } }, "sp_dyn_imrt_in": { "title": "Mortality rate, infant (per 1,000 live births)", "description_from_producer": "Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nGiven that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SP.DYN.IMRT.IN (https://data.worldbank.org/indicator/SP.DYN.IMRT.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.IMRT.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 live births", "display": { "unit": "per 1,000 live births" } }, "sp_dyn_imrt_ma_in": { "title": "Mortality rate, infant, male (per 1,000 live births)", "description_from_producer": "Infant mortality rate, male is the number of male infants dying before reaching one year of age, per 1,000 male live births in a given year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates of neonatal, infant, and child mortality tend to vary by source and method for a given time and place. Years for available estimates also vary by country, making comparisons across countries and over time difficult. To make neonatal, infant, and child mortality estimates comparable and to ensure consistency across estimates by different agencies, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME), which comprises the United Nations Children's Fund (UNICEF), the World Health Organization (WHO), the World Bank, the United Nations Population Division, and other universities and research institutes, developed and adopted a statistical method that uses all available information to reconcile differences. The method uses statistical models to obtain a best estimate trend line by fitting a country-specific regression model of mortality rates against their reference dates.\nStatistical concept(s): The main sources of mortality data are vital registration systems and direct or indirect estimates based on sample surveys or censuses. A \"complete\" vital registration system - covering at least 90 percent of vital events in the population - is the best source of age-specific mortality data.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nComplete vital registration systems are fairly uncommon in developing countries. Thus estimates must be obtained from sample surveys or derived by applying indirect estimation techniques to registration, census, or survey data. Survey data are subject to recall error, and surveys estimating infant/child deaths require large samples because households in which a birth has occurred during a given year cannot ordinarily be preselected for sampling. Indirect estimates rely on model life tables that may be inappropriate for the population concerned. Extrapolations based on outdated surveys may not be reliable for monitoring changes in health status or for comparative analytical work.\n\n### Other notes:\nGiven that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.\n\nAggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.", "origins": [ { "producer": "UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, UN Population Division), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Inter-agency Group for Child Mortality Estimation, UN Children's Fund (UNICEF), uri: www.childmortality.org;\nWorld Health Organization (WHO), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nWorld Bank (WB), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation;\nUnited Nations (UN), uri: www.childmortality.org, note: UN Inter-agency Group for Child Mortality Estimation, publisher: UN Population Division. Indicator SP.DYN.IMRT.MA.IN (https://data.worldbank.org/indicator/SP.DYN.IMRT.MA.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.IMRT.MA.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 1,000 live births", "display": { "unit": "per 1,000 live births" } }, "sp_dyn_le00_fe_in": { "title": "Life expectancy at birth, female (years)", "description_from_producer": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nAnnual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data.", "origins": [ { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs);\nDemographic Statistics, Eurostat (ESTAT). Indicator SP.DYN.LE00.FE.IN (https://data.worldbank.org/indicator/SP.DYN.LE00.FE.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.LE00.FE.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "years", "short_unit": "years", "display": { "shortUnit": "years", "unit": "years" } }, "sp_dyn_le00_in": { "title": "Life expectancy at birth, total (years)", "description_from_producer": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates, or derived from male and female life expectancy at birth.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nAnnual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), uri: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), note: Derived from male and female life expectancy at birth from sources such as statistical databases and publications from national statistical offices.;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from male and female life expectancy at birth from sources such as Eurostat: Demographic Statistics. Indicator SP.DYN.LE00.IN (https://data.worldbank.org/indicator/SP.DYN.LE00.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.LE00.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "years", "short_unit": "years", "display": { "shortUnit": "years", "unit": "years" } }, "sp_dyn_le00_ma_in": { "title": "Life expectancy at birth, male (years)", "description_from_producer": "Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Life expectancy at birth is derived from life tables and is based on sex- and age-specific death rates.\nStatistical concept(s): Life expectancy at birth used here is the average number of years a newborn is expected to live if mortality patterns at the time of its birth remain constant in the future. It reflects the overall mortality level of a population, and summarizes the mortality pattern that prevails across all age groups in a given year. It is calculated in a period life table which provides a snapshot of a population's mortality pattern at a given time. It therefore does not reflect the mortality pattern that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n\n\nHigh mortality in young age groups significantly lowers the life expectancy at birth. But if a person survives his/her childhood of high mortality, he/she may live much longer. For example, in a population with a life expectancy at birth of 50, there may be few people dying at age 50. The life expectancy at birth may be low due to the high childhood mortality so that once a person survives his/her childhood, he/she may live much longer than 50 years.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. Because data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. And they are among the indicators most frequently used to compare socioeconomic development across countries.\n\n### Limitations and exceptions:\nAnnual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data.", "origins": [ { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs);\nDemographic Statistics, Eurostat (ESTAT). Indicator SP.DYN.LE00.MA.IN (https://data.worldbank.org/indicator/SP.DYN.LE00.MA.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.LE00.MA.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "years", "short_unit": "years", "display": { "shortUnit": "years", "unit": "years" } }, "sp_dyn_tfrt_in": { "title": "Fertility rate, total (births per woman)", "description_from_producer": "Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Total fertility rate is the sum of the age-specific fertility rates (multiplied by five, if the age-specific fertility rates are for 5-year age groups).\nStatistical concept(s): Total fertility rates are based on data on registered live births from vital registration systems or, in the absence of such systems, from censuses or sample surveys. The estimated rates are generally considered reliable measures of fertility in the recent past. Where no empirical information on age-specific fertility rates is available, a model is used to estimate the share of births to adolescents. For countries without reliable vital registration systems fertility rates are generally based on extrapolations from trends observed in censuses or surveys from earlier years.\n\n### Development relevance:\nReproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n### Limitations and exceptions:\nAnnual data series from United Nations Population Division's World Population Prospects are interpolated data from 5-year period data. Therefore they may not reflect real events as much as observed data.\n\n### Other notes:\nRelevance to gender indicator: it can indicate the status of women within households and a woman\u2019s decision about the number and spacing of children.", "origins": [ { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs);\nDemographic Statistics, Eurostat (ESTAT). Indicator SP.DYN.TFRT.IN (https://data.worldbank.org/indicator/SP.DYN.TFRT.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.TFRT.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "births per woman", "short_unit": "births", "display": { "shortUnit": "births", "unit": "births per woman" } }, "sp_dyn_to65_fe_zs": { "title": "Survival to age 65, female (% of cohort)", "description_from_producer": "Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated using number of survivors, l(x), at exact age x in a female period life table. The formula is: (l(65))/(l(0))*100.\nStatistical concept(s): Survival to age 65 is calculated in a period life table. It provides a population's mortality level up to age 65 at a given time. It therefore does not reflect the mortality level that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. The lower the age specific mortality rates before age 65, the higher the proportion of people survive to age 65.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.DYN.TO65.FE.ZS (https://data.worldbank.org/indicator/SP.DYN.TO65.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.TO65.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of cohort", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of cohort" } }, "sp_dyn_to65_ma_zs": { "title": "Survival to age 65, male (% of cohort)", "description_from_producer": "Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated using number of survivors l(x) at exact age x, in a male period life table. The formula is: (l(65))/(l(0))*100.\nStatistical concept(s): Survival to age 65 is calculated in a period life table. It provides a population's mortality level up to age 65 at a given time. It therefore does not reflect the mortality level that a person actually experiences during his/her life, which can be calculated in a cohort life table.\n\n### Development relevance:\nMortality rates for different age groups (infants, children, and adults) and overall mortality indicators (life expectancy at birth or survival to a given age) are important indicators of health status in a country. The lower the age specific mortality rates before age 65, the higher the proportion of people survive to age 65.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.DYN.TO65.MA.ZS (https://data.worldbank.org/indicator/SP.DYN.TO65.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.TO65.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of cohort", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of cohort" } }, "sp_dyn_wfrt": { "title": "Wanted fertility rate (births per woman)", "description_from_producer": "Wanted fertility rate is an estimate of what the total fertility rate would be if all unwanted births were avoided.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data is calculated by summing the seven age-specific wanted fertility rates, multiplying the result by five, and dividing by 1000. A birth is considered wanted if the number of living children at the time of conception is less than the ideal number of children as reported by the respondent. Special responses such as \"don't know,\" \"up to God,\" or other non-numeric responses for the ideal number of children are assumed to indicate a high ideal number of children. For more details, please refer to the DHS website: https://dhsprogram.com/data/Guide-to-DHS-Statistics/Wanted_Fertility.htm", "origins": [ { "producer": "Demographic and Health Surveys (DHS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys. Indicator SP.DYN.WFRT (https://data.worldbank.org/indicator/SP.DYN.WFRT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.DYN.WFRT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "births per woman", "short_unit": "births", "display": { "shortUnit": "births", "unit": "births per woman" } }, "sp_hou_fema_zs": { "title": "Female headed households (% of households with a female head)", "description_from_producer": "Female headed households refers to the percentage of households that are headed by females.\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated by the number of households headed by women divided by the total number of households.\n\n\n\n\n\n\n\n\n\n\n\nThe definition of a household is a person or group of related or unrelated persons who live together in the same dwelling unit(s), who acknowledge one adult male or female as the head of the household, who share the same housekeeping arrangements and who are considered a single unit.\nStatistical concept(s): The information on the characteristics of household head (e.g., sex, age) is collected the household questionnaire in the Demographic and Health Surveys (DHS). Typically, this data is obtained by detailing the connection of each member of the household to a designated central figure, who is considered the primary reference for the household.\n\n### Development relevance:\nThe composition of households plays a pivotal role in determining the well-being of families and individuals. Research from multiple developed countries indicates that female-headed households, especially those with single mothers, face a higher risk of poverty than those with two parents (United Nations, \"Patterns and trends in household size and composition: Evidence from a United Nations dataset,\" 2019). Understanding the diversity in household structures across various populations is essential for achieving Sustainable Development Goal 1, which is dedicated to eradicating poverty in all its forms.\n\n### Limitations and exceptions:\nThe definition of female-headed household differs greatly across countries, making cross-country comparison difficult. In some cases it is assumed that a woman cannot be the head of any household with an adult male, because of sex-biased stereotype. Caution should be used in interpreting the data.\n\n### Other notes:\nThe composition of a household plays a role in the determining other characteristics of a household, such as how many children are sent to school and the distribution of family income.", "origins": [ { "producer": "DHS Program (ICF) (DHS API), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "DHS API, Demographic and Health Surveys (DHS) Program, uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: HC_HHHD_H_FEM; \tIndicator name from the original source: Female-headed households, publisher: DHS Program (ICF), type: API, date accessed: 2024-06-14. Indicator SP.HOU.FEMA.ZS (https://data.worldbank.org/indicator/SP.HOU.FEMA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.HOU.FEMA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of households with a female head", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of households with a female head" } }, "sp_m15_2024_fe_zs": { "title": "Women who were first married by age 15 (% of women ages 20-24)", "description_from_producer": "Women who were first married by age 15 refers to the percentage of women ages 20-24 who were first married by age 15.\n\n### Statistical concept and methodology:\nMethodology: Number of women aged 20-24 who were first married or in union before age 15 divided by the total number of women aged 20-24 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). Additionally, other national household surveys and censuses contribute to the data. These figures are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): This indicator includes both formal marriages and informal cohabiting relationships. Informal relationships are usually defined as those where a couple lives together with the intention of a long-term relationship but without a formal civil or religious ceremony. The incidence of child marriage is assessed retrospectively among women who are past the risk of marrying as children. The age range of 20 to 24 years is conventionally used to reflect the current prevalence of child marriage.\n\n### Development relevance:\nAlthough the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A women\u2019s access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage.\n\n### Limitations and exceptions:\nThe measure of child marriage is designed to be retrospective, focusing on the age at first marriage among adult women who have already passed the risk period. Although it is feasible to assess the current marital status of girls under 15, this approach could underestimate the true extent of child marriage. This is because girls who are not married at the time of survey may still marry before reaching 15.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UNICEF and Demographic and Health Surveys (DHS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF Data, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_20-24_MRD_U15; \tIndicator name from the original source: Percentage of women (aged 20-24 years) married or in union before age 15, type: API;\nDHS API, Demographic and Health Surveys (DHS) Program, uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: MA_MBAG_W_B15; \tIndicator name from the original source: Women first married by exact age 15, type: API. Indicator SP.M15.2024.FE.ZS (https://data.worldbank.org/indicator/SP.M15.2024.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.M15.2024.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of women ages 20-24", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of women ages 20-24" } }, "sp_m18_2024_fe_zs": { "title": "Women who were first married by age 18 (% of women ages 20-24)", "description_from_producer": "Women who were first married by age 18 refers to the percentage of women ages 20-24 who were first married by age 18.\n\n### Statistical concept and methodology:\nMethodology: Number of women aged 20-24 who were first married or in union before age 18 divided by the total number of women aged 20-24 in the population multiplied by 100. The primary sources for this indicator are the Multiple Indicator Cluster Surveys (MICS) and the Demographic and Health Surveys (DHS). Additionally, other national household surveys and censuses contribute to the data. These figures are compiled by UNICEF, which coordinates with countries to gather the information.\nStatistical concept(s): This indicator includes both formal marriages and informal cohabiting relationships. Informal relationships are usually defined as those where a couple lives together with the intention of a long-term relationship but without a formal civil or religious ceremony. The incidence of child marriage is assessed retrospectively among women who are past the risk of marrying as children. The age range of 20 to 24 years is conventionally used to reflect the current prevalence of child marriage.\n\n### Development relevance:\nAlthough the legal age of marriage is defined as 18 years in most countries, the practice of child marriage remains widespread. A woman's access to education and later her employment opportunities as well as the nature and terms of her work are often compromised by this practice. Young married girls whose schooling is cut short often lack the knowledge and skills for formal work and are limited to occupations with lower incomes and inferior working conditions. Sustainable Development Goal 5 commits to eliminate the practice of child marriage.\n\n### Limitations and exceptions:\nThe measure of child marriage is designed to be retrospective, focusing on the age at first marriage among adult women who have already passed the risk period. Although it is feasible to assess the current marital status of girls under 18, this approach could underestimate the true extent of child marriage. This is because girls who are not married at the time of survey may still marry before reaching 18.\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 5.3.1[https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "UNICEF and The DHS Program (ICF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNICEF Data, UN Children's Fund (UNICEF), uri: https://sdmx.data.unicef.org/overview.html, note: Indicator code from the original source: PT_F_20-24_MRD_U18; \tIndicator name from the original source: Percentage of women (aged 20-24 years) married or in union before age 18, type: API;\nDHS API, Demographic and Health Surveys (DHS) Program, uri: https://api.dhsprogram.com/#/index.html, note: Indicator code from the original source: MA_MBAG_W_B18; \tIndicator name from the original source: Women first married by exact age 18, type: API. Indicator SP.M18.2024.FE.ZS (https://data.worldbank.org/indicator/SP.M18.2024.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.M18.2024.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of women ages 20-24", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of women ages 20-24" } }, "sp_mtr_1519_zs": { "title": "Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)", "description_from_producer": "Teenage mothers are the percentage of women ages 15-19 who already have children or are currently pregnant.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The data represents the combined percentage of women aged 15-19 who are mothers and those who are pregnant with their first child. This information is gathered through individual interviews with women of reproductive age during household surveys, including Demographic and Health Surveys. For more details, please refer to the DHS website: https://dhsprogram.com/data/Guide-to-DHS-Statistics/Teenage_Pregnancy_and_Motherhood.htm\n\n### Development relevance:\nHaving a child during the teenage years limits girls' opportunities for better education, jobs, and income. Pregnancy is more likely to be unintended during the teenage years, and births are more likely to be premature and are associated with greater risks of complications during delivery and of death. In many countries maternal mortality is a leading cause of death among women of reproductive age, although most of those deaths are preventable. Infants of adolescent mothers are also more likely to have low birth weight, which can have a long-term impact on their health and development. Complications from pregnancy and childbirth are the leading cause of death among girls aged 15-19 years in many low- and middle-income countries.", "origins": [ { "producer": "Demographic and Health Surveys (DHS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Demographic and Health Surveys. Indicator SP.MTR.1519.ZS (https://data.worldbank.org/indicator/SP.MTR.1519.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.MTR.1519.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of women ages 15-19 who have had children or are currently pregnant", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of women ages 15-19 who have had children or are currently pregnant" } }, "sp_pop_0004_fe_5y": { "title": "Population ages 00-04, female (% of female population)", "description_from_producer": "Female population between the ages 0 to 4 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.0004.FE.5Y (https://data.worldbank.org/indicator/SP.POP.0004.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.0004.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_0004_ma_5y": { "title": "Population ages 00-04, male (% of male population)", "description_from_producer": "Male population between the ages 0 to 4 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.0004.MA.5Y (https://data.worldbank.org/indicator/SP.POP.0004.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.0004.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_0014_fe_in": { "title": "Population ages 0-14, female", "description_from_producer": "Female population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.\n\n### Other notes:\nRelevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.0014.FE.IN (https://data.worldbank.org/indicator/SP.POP.0014.FE.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.0014.FE.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_0014_fe_zs": { "title": "Population ages 0-14, female (% of female population)", "description_from_producer": "Female population between the ages 0 to 14 as a percentage of the total female population. Population is based on the de facto definition of population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.\n\n### Other notes:\nRelevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.0014.FE.ZS (https://data.worldbank.org/indicator/SP.POP.0014.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.0014.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_pop_0014_ma_in": { "title": "Population ages 0-14, male", "description_from_producer": "Male population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.0014.MA.IN (https://data.worldbank.org/indicator/SP.POP.0014.MA.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.0014.MA.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_0014_ma_zs": { "title": "Population ages 0-14, male (% of male population)", "description_from_producer": "Male population between the ages 0 to 14 as a percentage of the total male population. Population is based on the de facto definition of population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.0014.MA.ZS (https://data.worldbank.org/indicator/SP.POP.0014.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.0014.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_pop_0014_to": { "title": "Population ages 0-14, total", "description_from_producer": "Total population between the ages 0 to 14. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.", "origins": [ { "producer": "World Bank staff estimates based on data from the UN Population Division", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB), note: Staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's World Population Prospects., publisher: World Bank staff estimates using the World Bank's total population and age/sex distributions of the United Nations Population Division's data.;\nWorld Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.0014.TO (https://data.worldbank.org/indicator/SP.POP.0014.TO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.0014.TO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_0014_to_zs": { "title": "Population ages 0-14 (% of total population)", "description_from_producer": "Population between the ages 0 to 14 as a percentage of the total population. Population is based on the de facto definition of population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.\n\n### Limitations and exceptions:\nBecause the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "United Nations Population Division. World Population Prospects., United Nations Population Division, uri: https://population.un.org/wpp/, publisher: United Nations Population Division. Indicator SP.POP.0014.TO.ZS (https://data.worldbank.org/indicator/SP.POP.0014.TO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.0014.TO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_pop_0509_fe_5y": { "title": "Population ages 05-09, female (% of female population)", "description_from_producer": "Female population between the ages 5 to 9 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.0509.FE.5Y (https://data.worldbank.org/indicator/SP.POP.0509.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.0509.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_0509_ma_5y": { "title": "Population ages 05-09, male (% of male population)", "description_from_producer": "Male population between the ages 5 to 9 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.0509.MA.5Y (https://data.worldbank.org/indicator/SP.POP.0509.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.0509.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_1014_fe_5y": { "title": "Population ages 10-14, female (% of female population)", "description_from_producer": "Female population between the ages 10 to 14 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.1014.FE.5Y (https://data.worldbank.org/indicator/SP.POP.1014.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.1014.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_1014_ma_5y": { "title": "Population ages 10-14, male (% of male population)", "description_from_producer": "Male population between the ages 10 to 14 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.1014.MA.5Y (https://data.worldbank.org/indicator/SP.POP.1014.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.1014.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_1519_fe_5y": { "title": "Population ages 15-19, female (% of female population)", "description_from_producer": "Female population between the ages 15 to 19 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.1519.FE.5Y (https://data.worldbank.org/indicator/SP.POP.1519.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.1519.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_1519_ma_5y": { "title": "Population ages 15-19, male (% of male population)", "description_from_producer": "Male population between the ages 15 to 19 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.1519.MA.5Y (https://data.worldbank.org/indicator/SP.POP.1519.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.1519.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_1564_fe_in": { "title": "Population ages 15-64, female", "description_from_producer": "Female population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.\n\n### Other notes:\nRelevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.1564.FE.IN (https://data.worldbank.org/indicator/SP.POP.1564.FE.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.1564.FE.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_1564_fe_zs": { "title": "Population ages 15-64, female (% of female population)", "description_from_producer": "Female population between the ages 15 to 64 as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.\n\n### Other notes:\nRelevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.1564.FE.ZS (https://data.worldbank.org/indicator/SP.POP.1564.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.1564.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_pop_1564_ma_in": { "title": "Population ages 15-64, male", "description_from_producer": "Male population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.1564.MA.IN (https://data.worldbank.org/indicator/SP.POP.1564.MA.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.1564.MA.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_1564_ma_zs": { "title": "Population ages 15-64, male (% of male population)", "description_from_producer": "Male population between the ages 15 to 64 as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.1564.MA.ZS (https://data.worldbank.org/indicator/SP.POP.1564.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.1564.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_pop_1564_to": { "title": "Population ages 15-64, total", "description_from_producer": "Total population between the ages 15 to 64. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.1564.TO (https://data.worldbank.org/indicator/SP.POP.1564.TO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.1564.TO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_1564_to_zs": { "title": "Population ages 15-64 (% of total population)", "description_from_producer": "Total population between the ages 15 to 64 as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.\n\n### Limitations and exceptions:\nBecause the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.1564.TO.ZS (https://data.worldbank.org/indicator/SP.POP.1564.TO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.1564.TO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_pop_2024_fe_5y": { "title": "Population ages 20-24, female (% of female population)", "description_from_producer": "Female population between the ages 20 to 24 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.2024.FE.5Y (https://data.worldbank.org/indicator/SP.POP.2024.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.2024.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_2024_ma_5y": { "title": "Population ages 20-24, male (% of male population)", "description_from_producer": "Male population between the ages 20 to 24 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.2024.MA.5Y (https://data.worldbank.org/indicator/SP.POP.2024.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.2024.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_2529_fe_5y": { "title": "Population ages 25-29, female (% of female population)", "description_from_producer": "Female population between the ages 25 to 29 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.2529.FE.5Y (https://data.worldbank.org/indicator/SP.POP.2529.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.2529.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_2529_ma_5y": { "title": "Population ages 25-29, male (% of male population)", "description_from_producer": "Male population between the ages 25 to 29 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.2529.MA.5Y (https://data.worldbank.org/indicator/SP.POP.2529.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.2529.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_3034_fe_5y": { "title": "Population ages 30-34, female (% of female population)", "description_from_producer": "Female population between the ages 30 to 34 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.3034.FE.5Y (https://data.worldbank.org/indicator/SP.POP.3034.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.3034.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_3034_ma_5y": { "title": "Population ages 30-34, male (% of male population)", "description_from_producer": "Male population between the ages 30 to 34 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.3034.MA.5Y (https://data.worldbank.org/indicator/SP.POP.3034.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.3034.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_3539_fe_5y": { "title": "Population ages 35-39, female (% of female population)", "description_from_producer": "Female population between the ages 35 to 39 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.3539.FE.5Y (https://data.worldbank.org/indicator/SP.POP.3539.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.3539.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_3539_ma_5y": { "title": "Population ages 35-39, male (% of male population)", "description_from_producer": "Male population between the ages 35 to 39 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.3539.MA.5Y (https://data.worldbank.org/indicator/SP.POP.3539.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.3539.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_4044_fe_5y": { "title": "Population ages 40-44, female (% of female population)", "description_from_producer": "Female population between the ages 40 to 44 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.4044.FE.5Y (https://data.worldbank.org/indicator/SP.POP.4044.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.4044.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_4044_ma_5y": { "title": "Population ages 40-44, male (% of male population)", "description_from_producer": "Male population between the ages 40 to 44 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.4044.MA.5Y (https://data.worldbank.org/indicator/SP.POP.4044.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.4044.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_4549_fe_5y": { "title": "Population ages 45-49, female (% of female population)", "description_from_producer": "Female population between the ages 45 to 49 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.4549.FE.5Y (https://data.worldbank.org/indicator/SP.POP.4549.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.4549.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_4549_ma_5y": { "title": "Population ages 45-49, male (% of male population)", "description_from_producer": "Male population between the ages 45 to 49 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.4549.MA.5Y (https://data.worldbank.org/indicator/SP.POP.4549.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.4549.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_5054_fe_5y": { "title": "Population ages 50-54, female (% of female population)", "description_from_producer": "Female population between the ages 50 to 54 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.5054.FE.5Y (https://data.worldbank.org/indicator/SP.POP.5054.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.5054.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_5054_ma_5y": { "title": "Population ages 50-54, male (% of male population)", "description_from_producer": "Male population between the ages 50 to 54 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.5054.MA.5Y (https://data.worldbank.org/indicator/SP.POP.5054.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.5054.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_5559_fe_5y": { "title": "Population ages 55-59, female (% of female population)", "description_from_producer": "Female population between the ages 55 to 59 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.5559.FE.5Y (https://data.worldbank.org/indicator/SP.POP.5559.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.5559.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_5559_ma_5y": { "title": "Population ages 55-59, male (% of male population)", "description_from_producer": "Male population between the ages 55 to 59 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.5559.MA.5Y (https://data.worldbank.org/indicator/SP.POP.5559.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.5559.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_6064_fe_5y": { "title": "Population ages 60-64, female (% of female population)", "description_from_producer": "Female population between the ages 60 to 64 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.6064.FE.5Y (https://data.worldbank.org/indicator/SP.POP.6064.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.6064.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_6064_ma_5y": { "title": "Population ages 60-64, male (% of male population)", "description_from_producer": "Male population between the ages 60 to 64 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.6064.MA.5Y (https://data.worldbank.org/indicator/SP.POP.6064.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.6064.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_6569_fe_5y": { "title": "Population ages 65-69, female (% of female population)", "description_from_producer": "Female population between the ages 65 to 69 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.6569.FE.5Y (https://data.worldbank.org/indicator/SP.POP.6569.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.6569.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_6569_ma_5y": { "title": "Population ages 65-69, male (% of male population)", "description_from_producer": "Male population between the ages 65 to 69 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.6569.MA.5Y (https://data.worldbank.org/indicator/SP.POP.6569.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.6569.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_65up_fe_in": { "title": "Population ages 65 and above, female", "description_from_producer": "Female population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.\n\n### Other notes:\nRelevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.65UP.FE.IN (https://data.worldbank.org/indicator/SP.POP.65UP.FE.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.65UP.FE.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_65up_fe_zs": { "title": "Population ages 65 and above, female (% of female population)", "description_from_producer": "Female population 65 years of age or older as a percentage of the total female population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.\n\n### Other notes:\nRelevance to gender indicator: Knowing how many girls, adolescents and women there are in a population helps a country in determining its provision of services.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.65UP.FE.ZS (https://data.worldbank.org/indicator/SP.POP.65UP.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.65UP.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_pop_65up_ma_in": { "title": "Population ages 65 and above, male", "description_from_producer": "Male population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.65UP.MA.IN (https://data.worldbank.org/indicator/SP.POP.65UP.MA.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.65UP.MA.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_65up_ma_zs": { "title": "Population ages 65 and above, male (% of male population)", "description_from_producer": "Male population 65 years of age or older as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.65UP.MA.ZS (https://data.worldbank.org/indicator/SP.POP.65UP.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.65UP.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_pop_65up_to": { "title": "Population ages 65 and above, total", "description_from_producer": "Total population 65 years of age or older. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.65UP.TO (https://data.worldbank.org/indicator/SP.POP.65UP.TO). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.65UP.TO", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_65up_to_zs": { "title": "Population ages 65 and above (% of total population)", "description_from_producer": "Population ages 65 and above as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n\n\nThis indicator is used for calculating age dependency ratio (percent of working-age population). The age dependency ratio is the ratio of the sum of the population aged 0-14 and the population aged 65 and above to the population aged 15-64. In many developing countries, the once rapidly growing population group of the under-15 population is shrinking. As a result, high fertility rates, together with declining mortality rates, are now reflected in the larger share of the 65 and older population.\n\n### Limitations and exceptions:\nBecause the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.65UP.TO.ZS (https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_pop_7074_fe_5y": { "title": "Population ages 70-74, female (% of female population)", "description_from_producer": "Female population between the ages 70 to 74 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.7074.FE.5Y (https://data.worldbank.org/indicator/SP.POP.7074.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.7074.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_7074_ma_5y": { "title": "Population ages 70-74, male (% of male population)", "description_from_producer": "Male population between the ages 70 to 74 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.7074.MA.5Y (https://data.worldbank.org/indicator/SP.POP.7074.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.7074.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_7579_fe_5y": { "title": "Population ages 75-79, female (% of female population)", "description_from_producer": "Female population between the ages 75 to 79 as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.7579.FE.5Y (https://data.worldbank.org/indicator/SP.POP.7579.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.7579.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_7579_ma_5y": { "title": "Population ages 75-79, male (% of male population)", "description_from_producer": "Male population between the ages 75 to 79 as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.7579.MA.5Y (https://data.worldbank.org/indicator/SP.POP.7579.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.7579.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_80up_fe_5y": { "title": "Population ages 80 and above, female (% of female population)", "description_from_producer": "Female population between the ages 80 and above as a percentage of the total female population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.80UP.FE.5Y (https://data.worldbank.org/indicator/SP.POP.80UP.FE.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.80UP.FE.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of female population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of female population" } }, "sp_pop_80up_ma_5y": { "title": "Population ages 80 and above, male (% of male population)", "description_from_producer": "Male population between the ages 80 and above as a percentage of the total male population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Proportion of population by age and/or sex describes the proportion of the population in the category out of total (or male total or female total). Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the age/sex distribution of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.80UP.MA.5Y (https://data.worldbank.org/indicator/SP.POP.80UP.MA.5Y). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.80UP.MA.5Y", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of male population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of male population" } }, "sp_pop_brth_mf": { "title": "Sex ratio at birth (male births per female births)", "description_from_producer": "Sex ratio at birth refers to male births per female births.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Sex ratio at birth is calculated as number of male births divided by number of female births.\nStatistical concept(s): If the sex ratio at birth is greater than 1, it indicates more boys born that year than girls.\n\n### Development relevance:\nIn the absence of interference, it is expected that the sex ratio at birth is fairly stable within the range of 1.03 to 1.07 boys born per 1.00 girls. However, in some populations, the observed sex ratio at birth is well above this range because of sex-selection driven by the preference for sons over daughters.", "origins": [ { "producer": "United Nations Population Division (World Population Prospects), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division. Indicator SP.POP.BRTH.MF (https://data.worldbank.org/indicator/SP.POP.BRTH.MF). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.BRTH.MF", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "male births per female births", "short_unit": "male births", "display": { "shortUnit": "male births", "unit": "male births per female births" } }, "sp_pop_dpnd": { "title": "Age dependency ratio (% of working-age population)", "description_from_producer": "Age dependency ratio is the ratio of dependents--people younger than 15 or older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Age dependency ratio is calculated as 100 x (Population (0-14) + Population (65+)) / Population (15-64). Data are shown as the proportion of dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n### Limitations and exceptions:\nBecause the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source.\n\n### Other notes:\nRelevance to gender indicator: this indicator implies the dependency burden that the working-age population bears in relation to children and the elderly. Many times single or widowed women who are the sole caregiver of a household have a high dependency ratio.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.DPND (https://data.worldbank.org/indicator/SP.POP.DPND). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.DPND", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of working-age population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of working-age population" } }, "sp_pop_dpnd_ol": { "title": "Age dependency ratio, old (% of working-age population)", "description_from_producer": "Age dependency ratio, old, is the ratio of older dependents--people older than 64--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Age dependency ratio, old is calculated as 100 x (Population (65+)) / Population (15-64). Data are shown as the proportion of old dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n### Limitations and exceptions:\nBecause the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.DPND.OL (https://data.worldbank.org/indicator/SP.POP.DPND.OL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.DPND.OL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of working-age population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of working-age population" } }, "sp_pop_dpnd_yg": { "title": "Age dependency ratio, young (% of working-age population)", "description_from_producer": "Age dependency ratio, young, is the ratio of younger dependents--people younger than 15--to the working-age population--those ages 15-64. Data are shown as the proportion of dependents per 100 working-age population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Age dependency ratio, young is calculated as 100 x (Population (0-14)) / Population (15-64). Data are shown as the proportion of young dependents per 100 working-age population.\nStatistical concept(s): Dependency ratios capture variations in the proportions of children, elderly people, and working-age people in the population that imply the dependency burden that the working-age population bears in relation to children and the elderly. But dependency ratios show only the age composition of a population, not economic dependency. Some children and elderly people are part of the labor force, and many working-age people are not.\n\n\n\nAge structure in the World Bank's population estimates is based on the age structure in United Nations Population Division's World Population Prospects.\n\n### Development relevance:\nPatterns of development in a country are partly determined by the age composition of its population. Different age groups have different impacts on both the environment and on infrastructure needs. Therefore the age structure of a population is useful for analyzing resource use and formulating future policy and planning goals with regards infrastructure and development.\n\n### Limitations and exceptions:\nBecause the five-year age group is the cohort unit and five-year period data are used in the United Nations Population Division's World Population Prospects, interpolations to obtain annual data or single age structure may not reflect actual events or age composition. For more information, see the original source.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.DPND.YG (https://data.worldbank.org/indicator/SP.POP.DPND.YG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.DPND.YG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of working-age population", "short_unit": "%", "display": { "name": "Age dependency ratio, young", "shortUnit": "%", "unit": "% of working-age population" }, "presentation": { "title_public": "Age dependency ratio, young (% of working-age population)" } }, "sp_pop_grow": { "title": "Population growth (annual %)", "description_from_producer": "Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The growth rate is computed using the exponential growth formula:\n\n\n\nr = ln(pn/p0)/n,\n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\n\n\n\nFor information on total population from which the growth rates are calculated, see total population (SP.POP.TOTL).\nStatistical concept(s): Total population growth rates are calculated on the assumption that rate of growth is constant between two points in time.\n\n### Development relevance:\nIncreases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure.", "origins": [ { "producer": "United Nations Population Division, national statistical offices, Eurostat, and United Nations Statistical Division, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), note: Derived from total population, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), note: Derived from total population;\nDemographic Statistics, Eurostat (ESTAT), note: Derived from total population;\nPopulation and Vital Statistics Report (various years), United Nations (UN), note: Derived from total population, publisher: UN Statistical Division. Indicator SP.POP.GROW (https://data.worldbank.org/indicator/SP.POP.GROW). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.GROW", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" } }, "sp_pop_scie_rd_p6": { "title": "Researchers in R&D (per million people)", "description_from_producer": "The number of researchers engaged in Research &Development (R&D), expressed as per million. Researchers are professionals who conduct research and improve or develop concepts, theories, models techniques instrumentation, software of operational methods. R&D covers basic research, applied research, and experimental development.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated by taking the number of researchers in a specified year, dividing it by the total population\u2014referencing the mid-year population figure\u2014and then multiplying the result by one million.\n\n\n\n\n\n\n\n\n\nData are collected through national research and experimental development (R&D) surveys, either by the national statistical office or a line ministry (such as the Ministry for Science and Technology). The data compilers are the UNESCO Institute for Statistics (UIS), Organisation for Economic Co-operation and Development (OECD), Eurostat (Statistical Office of the European Union) and the Network on Science and Technology Indicators \u2013 Ibero-American and Inter-American (RICYT), African Science, Technology and Innovation (STI) Indicators Initiative (ASTII) of the African Union Development Agency-NEPAD (AUDA-NEPAD).\nStatistical concept(s): Researchers are professionals engaged in the conception or creation of new knowledge, products, processes, methods and systems, as well as in the management of these projects. Students studying at the master\u2019s or doctoral level (ISCED2011 level 7 or 8) engaged in R&D are included.\n\n\n\n\n\n\n\n\n\nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development (Reference: https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en).\n\n\n\n\n\n\n\n\n\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view.\n\n\n\n\n\n\n\n\n\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n\n\n\n\n\n\n\n\n\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\n\n\n\n\n\n\n\n\n\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n\n\n\n\n1. Natural sciences;\n\n\n\n\n2. Engineering and technology;\n\n\n\n\n3. Medical and health sciences;\n\n\n\n\n4. Agricultural sciences;\n\n\n\n\n5. Social sciences;\n\n\n\n\n6. Humanities and the arts.\n\n\n\n\n\n\n\n\n\nData are for full-time equivalent (FTE); the FTE of R&D personnel is defined as the ratio of working hours actually spent on R&D during a specific reference period (usually a calendar year) divided by the total number of hours conventionally worked in the same period by an individual or by a group.\n\n\n\n\n\n\n\n\n\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors.\n\n### Development relevance:\nScience, technology, and innovation constitute pivotal elements for achieving sustainable growth. Sustainable Development Goal (SDG) target 9.5 is dedicated to the enhancement of scientific research and the advancement of technological capabilities within industrial sectors, with a particular focus on low- and middle-income countries. Furthermore, this target encompasses the objective of augmenting the cadre of research and development personnel, as well as escalating expenditures in research.\n\n### Limitations and exceptions:\nEstimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers.", "origins": [ { "producer": "UNESCO UIS Stat Bulk Data Download Service, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Stat Bulk Data Download Service, UN Educational, Scientific and Cultural Organization (UNESCO), uri: https://databrowser.uis.unesco.org/resources/bulk, publisher: UNESCO Institute for Statistics (UIS), date accessed: 2025-03-26, date published: 2025-02. Indicator SP.POP.SCIE.RD.P6 (https://data.worldbank.org/indicator/SP.POP.SCIE.RD.P6). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.SCIE.RD.P6", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per million people", "display": { "unit": "per million people" } }, "sp_pop_tech_rd_p6": { "title": "Technicians in R&D (per million people)", "description_from_producer": "The number of technicians participated in Research & Development (R&D), expressed as per million. Technicians and equivalent staff are people who perform scientific and technical tasks involving the application of concepts and operational methods, normally under the supervision of researchers. R&D covers basic research, applied research, and experimental development.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Technicians in research and development (R&D) are persons whose main tasks require technical knowledge and experience in one or more fields of engineering, physical and life sciences, or social sciences and humanities.\n\nThe OECD's Frascati Manual defines research and experimental development as \"creative work undertaken on a systemic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and the use of this stock of knowledge to devise new applications.\" R&D covers basic research, applied research, and experimental development.\n\n(1) Basic research - Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts, without any particular application or use in view.\n\n(2) Applied research - Applied research is also original investigation undertaken in order to acquire new knowledge; it is, however, directed primarily towards a specific practical aim or objective.\n\n(3) Experimental development - Experimental development is systematic work, drawing on existing knowledge gained from research and/or practical experience, which is directed to producing new materials, products or devices, to installing new processes, systems and services, or to improving substantially those already produced or installed.\n\nThe fields of science and technology used to classify R&D according to the Revised Fields of Science and Technology Classification are:\n1. Natural sciences;\n2. Engineering and technology;\n3. Medical and health sciences;\n4. Agricultural sciences;\n5. Social sciences;\n6. Humanities and the arts.\n\nData are for full-time equivalent (FTE); the FTE of R&D personnel is defined as the ratio of working hours actually spent on R&D during a specific reference period (usually a calendar year) divided by the total number of hours conventionally worked in the same period by an individual or by a group.\n\nThe data are obtained through statistical surveys which are regularly conducted at national level covering R&D performing entities in the private and public sectors.\n\n### Limitations and exceptions:\nEstimates of the resources allocated to R&D are affected by national characteristics such as the periodicity and coverage of national R&D surveys across institutional sectors and industries; and the use of different sampling and estimation methods. R&D typically involves a few large performers, hence R&D surveys use various techniques to maintain up-to-date registers of known performers, while attempting to identify new or occasional performers.", "origins": [ { "producer": "UNESCO Institute for Statistics (UIS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNESCO Institute of Statistics (UIS), UN Educational, Scientific and Cultural Organization (UNESCO), uri: http://uis.unesco.org, note: Data as of March 2021, publisher: UNESCO Institute of Statistics (UIS). Indicator SP.POP.TECH.RD.P6 (https://data.worldbank.org/indicator/SP.POP.TECH.RD.P6). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TECH.RD.P6", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per million people", "display": { "unit": "per million people" } }, "sp_pop_totl": { "title": "Population, total", "description_from_producer": "Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Population estimates are usually based on national population censuses, and estimates of fertility, mortality and migration.\n\n\n\nErrors and undercounting in census occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census.\n\n\n\nThe quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data.\n\n\n\nThe currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census.\n\n\n\nThe United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems.\n\n\n\nInternational migration is the only other factor besides birth and death rates that directly determines a country's population change. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect.\n\n\n\nOne of the major data sources of this indicator is UN Population Division's World Population Prospects, which use the cohort component method to produce population estimates and projections.\n\n\n\nPopulation projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant.\nStatistical concept(s): Estimates of total population describe the size of total population. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nIncreases in human population, whether as a result of immigration or more births than deaths, can impact natural resources and social infrastructure. This can place pressure on a country's sustainability. A significant growth in population will negatively impact the availability of land for agricultural production, and will aggravate demand for food, energy, water, social services, and infrastructure. On the other hand, decreasing population size - a result of fewer births than deaths, and people moving out of a country - can impact a government's commitment to maintain services and infrastructure.\n\n### Limitations and exceptions:\nCurrent population estimates for developing countries that lack (i) reliable recent census data, and (ii) pre- and post-census estimates for countries with census data, are provided by the United Nations Population Division and other agencies.\n\n\n\nThe cohort component method - a standard method for estimating and projecting population - requires fertility, mortality, and net migration data, often collected from sample surveys, which can be small or limited in coverage. Population estimates are from demographic modeling and so are susceptible to biases and errors from shortcomings in both the model and the data.\n\n\n\nBecause future trends cannot be known with certainty, population projections have a wide range of uncertainty.\n\n### Other notes:\nRelevance to gender indicator: disaggregating the population composition by gender will help a country in projecting its demand for social services on a gender basis.", "origins": [ { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_totl_fe_in": { "title": "Population, female", "description_from_producer": "Female population is based on the de facto definition of population, which counts all female residents regardless of legal status or citizenship.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nFemales comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.TOTL.FE.IN (https://data.worldbank.org/indicator/SP.POP.TOTL.FE.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL.FE.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_totl_fe_zs": { "title": "Population, female (% of total population)", "description_from_producer": "Female population is the percentage of the population that is female. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nFemales comprise almost one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision. Indicator SP.POP.TOTL.FE.ZS (https://data.worldbank.org/indicator/SP.POP.TOTL.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_pop_totl_ma_in": { "title": "Population, male", "description_from_producer": "Male population is based on the de facto definition of population, which counts all male residents regardless of legal status or citizenship.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nMales comprise about one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB). Indicator SP.POP.TOTL.MA.IN (https://data.worldbank.org/indicator/SP.POP.TOTL.MA.IN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL.MA.IN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_pop_totl_ma_zs": { "title": "Population, male (% of total population)", "description_from_producer": "Male population is the percentage of the population that is male. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Estimates are calculated using the World Bank's total population and age/sex distributions of the population in United Nations Population Division's World Population Prospects.\nStatistical concept(s): Estimates of population by age and/or sex describe the size of the population in the category. Population estimates are dependent on the demographic components of change that are fertility, mortality and migration. As the size of population continues to change throughout the time even within a year, a reference time in the year that the estimate refers to is needed, such as mid-year, end-year or beginning of year. The values shown are midyear estimates.\n\n### Development relevance:\nMales comprise about one-half of the world population. Female population relative to male population is a primary demographic indicator, reflecting historical events such as wars and the socio-demographic and ethno-cultural characteristics of the population.", "origins": [ { "producer": "United Nations Population Division and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2022 Revision. Indicator SP.POP.TOTL.MA.ZS (https://data.worldbank.org/indicator/SP.POP.TOTL.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_reg_brth_fe_zs": { "title": "Completeness of birth registration, female (%)", "description_from_producer": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.\n\n### Statistical concept and methodology:\nMethodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD).\n\n### Development relevance:\nSociety typically first acknowledges a child\u2019s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person\u2019s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to \u201cBy 2030, provide legal identity for all, including birth registration\u201d.\n\n### Limitations and exceptions:\nData on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9).\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Household surveys (DHS, MICS), compiled by UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF. Indicator SP.REG.BRTH.FE.ZS (https://data.worldbank.org/indicator/SP.REG.BRTH.FE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.REG.BRTH.FE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sp_reg_brth_ma_zs": { "title": "Completeness of birth registration, male (%)", "description_from_producer": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.\n\n### Statistical concept and methodology:\nMethodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD).\n\n### Development relevance:\nSociety typically first acknowledges a child\u2019s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person\u2019s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to \u201cBy 2030, provide legal identity for all, including birth registration\u201d.\n\n### Limitations and exceptions:\nData on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9).\n\n### Other notes:\nThis is a sex-disaggregated indicator for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Household surveys (DHS, MICS), compiled by UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF. Indicator SP.REG.BRTH.MA.ZS (https://data.worldbank.org/indicator/SP.REG.BRTH.MA.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.REG.BRTH.MA.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sp_reg_brth_ru_zs": { "title": "Completeness of birth registration, rural (%)", "description_from_producer": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.\n\n### Statistical concept and methodology:\nMethodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD).\n\n### Development relevance:\nSociety typically first acknowledges a child\u2019s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person\u2019s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to \u201cBy 2030, provide legal identity for all, including birth registration\u201d.\n\n### Limitations and exceptions:\nData on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9).", "origins": [ { "producer": "Household surveys (DHS, MICS), compiled by UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF. Indicator SP.REG.BRTH.RU.ZS (https://data.worldbank.org/indicator/SP.REG.BRTH.RU.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.REG.BRTH.RU.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sp_reg_brth_ur_zs": { "title": "Completeness of birth registration, urban (%)", "description_from_producer": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.\n\n### Statistical concept and methodology:\nMethodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD).\n\n### Development relevance:\nSociety typically first acknowledges a child\u2019s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person\u2019s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to \u201cBy 2030, provide legal identity for all, including birth registration\u201d.\n\n### Limitations and exceptions:\nData on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9).\n\n### Other notes:\nThis is a disaggregated indicator (residence) for Sustainable Development Goal 16.9.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Household surveys (DHS, MICS), compiled by UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF. Indicator SP.REG.BRTH.UR.ZS (https://data.worldbank.org/indicator/SP.REG.BRTH.UR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.REG.BRTH.UR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sp_reg_brth_zs": { "title": "Completeness of birth registration (%)", "description_from_producer": "Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Birth registration refers to the permanent and official recording of a child's existence by some administrative levels of the State that is normally coordinated by a particular branch of the government.\n\n\n\nBirth registration estimates are primarily drawn from nationally representative household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic and Health Surveys (DHS). Other data sources include other national surveys, censuses and vital statistics from civil registration systems, and estimated coverage of birth registration within national civil registration systems from the United Nations Statistics Division (UNSD).\n\n### Development relevance:\nSociety typically first acknowledges a child\u2019s existence and identity through birth registration. The right to be recognized as a person before the law is a critical step in ensuring lifelong protection and is a prerequisite for exercising all other rights. A birth certificate, issued following birth registration, is an important identity document for children. Birth registration also serves a statistical purpose. Universal birth registration is an essential part of a system of vital statistics, which tracks the major milestones in a person\u2019s life - from birth to marriage and death. Such data are essential for planning and implementing development policies and programs, particularly in health, education, housing, water and sanitation, employment, agriculture and industrial production. The proportion of children under 5 years of age whose births have been registered with a civil authority is the official indicator for SDG Target 16.9, which sets out to \u201cBy 2030, provide legal identity for all, including birth registration\u201d.\n\n### Limitations and exceptions:\nData on the prevalence of birth registration is collected mainly through censuses, civil registration systems (CRVS) and household surveys. Civil registration systems that are functioning effectively compile vital statistics that are used to compare the estimated total number of births in a country with the absolute number of registered births during a given period. However, the systematic recording of births in many countries remains a serious challenge. In the absence of reliable administrative data, household surveys have become a key source of data to monitor levels and trends in birth registration. In most low- and middle-income countries, such surveys represent the sole source of this information.\n\n\n\nSubstantial differences can exist between CRVS coverage and birth registration levels as captured by household surveys. The differences are primarily because data from CRVS typically refer to the percentage of all births that have been registered (often within a specific timeframe) whereas household surveys often represent the percentage of children under age five whose births are registered. The latter (the level of registration among children under 5) is specified in the SDG indicator (16.9).\n\n### Other notes:\nThis is the Sustainable Development Goal indicator 16.9.1 [https://unstats.un.org/sdgs/metadata/].", "origins": [ { "producer": "Household surveys (DHS, MICS), compiled by UNICEF, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Household surveys, UN Children's Fund (UNICEF), note: Household surveys such as Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by UNICEF. Indicator SP.REG.BRTH.ZS (https://data.worldbank.org/indicator/SP.REG.BRTH.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.REG.BRTH.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "numDecimalPlaces": 1, "shortUnit": "%", "unit": "%" } }, "sp_reg_dths_zs": { "title": "Completeness of death registration with cause-of-death information (%)", "description_from_producer": "Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The completeness of death registration is calculated by dividing the total number of deaths registered with cause-of-death information in the vital registration system for a given country-year by the total estimated deaths for that year for the national population. The national level of completeness is provided by the National Statistical Offices of all countries and areas to the United Nations Statistics Division as part of the annual data collection for the United Nations Demographic Yearbook. Currently, the threshold used for compiling the data for this indicator is 75 percent for death registration.", "origins": [ { "producer": "World Health Organization (WHO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Global Health Observatory Data Repository/World Health Statistics, World Health Organization (WHO), uri: http://apps.who.int/gho/data/node.main.1?lang=en. Indicator SP.REG.DTHS.ZS (https://data.worldbank.org/indicator/SP.REG.DTHS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.REG.DTHS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "sp_rur_totl": { "title": "Rural population", "description_from_producer": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population. Aggregation of urban and rural population may not add up to total population because of different country coverages.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated using World Bank's total population estimates and rural ratios derived from the United Nations World Urbanization Prospects.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure.\n\n### Development relevance:\nThe rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area.\n\n### Limitations and exceptions:\nAggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population.", "origins": [ { "producer": "World Bank based on data from the UN Population Division", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions. Indicator SP.RUR.TOTL (https://data.worldbank.org/indicator/SP.RUR.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.RUR.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_rur_totl_zg": { "title": "Rural population growth (annual %)", "description_from_producer": "Annual rural population growth rate for year t is the exponential rate of growth of midyear rural population from year t-1 to t, expressed as a percentage. Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated from rural population estimates. The rural population estimates are calulcated using World Bank's total population estimates and rural ratios derived from the United Nations World Urbanization Prospects.\n\n\n\n\n\n\n\n\n\n\n\nThe growth rate is computed using the exponential growth formula:\n\n\n\n\n\n\n\n\n\n\n\nr = ln(pn/p0)/n,\n\n\n\n\n\n\n\n\n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure.\n\n### Development relevance:\nThe rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area.\n\n### Limitations and exceptions:\nAggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population.", "origins": [ { "producer": "World Bank based on data from the UN Population Division", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions. Indicator SP.RUR.TOTL.ZG (https://data.worldbank.org/indicator/SP.RUR.TOTL.ZG). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.RUR.TOTL.ZG", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" } }, "sp_rur_totl_zs": { "title": "Rural population (% of total population)", "description_from_producer": "Rural population refers to people living in rural areas as defined by national statistical offices. It is calculated as the difference between total population and urban population.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Percentages rural are calculated as the difference between 100 and the proportion of urban population in percentage.\nStatistical concept(s): Rural population is calculated as the difference between the total population and the urban population. Rural population is approximated as the midyear nonurban population. While a practical means of identifying the rural population, it is not a precise measure.\n\n### Development relevance:\nThe rural population is calculated using the urban share reported by the United Nations Population Division.\n\nThe two distinct images - isolated farm, thriving metropolis - represent poles on a continuum. Life changes along a variety of dimensions, moving from the most remote forest outpost through fields and pastures, past tiny hamlets, through small towns with weekly farm markets, into intensively cultivated areas near large towns and small cities, eventually reaching the center of a megacity. Along the way access to infrastructure, social services, and nonfarm employment increase, and with them population density and income.\n\nA 2005 World Bank Policy Research Paper proposes an operational definition of rurality based on population density and distance to large cities (Chomitz, Buys, and Thomas 2005). The report argues that these criteria are important gradients along which economic behavior and appropriate development interventions vary substantially. Where population densities are low, markets of all kinds are thin, and the unit cost of delivering most social services and many types of infrastructure is high. Where large urban areas are distant, farm-gate or factory-gate prices of outputs will be low and input prices will be high, and it will be difficult to recruit skilled people to public service or private enterprises. Thus, low population density and remoteness together define a set of rural areas that face special development challenges.\n\nCountries differ in the way they classify population as \"urban\" or \"rural.\"\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nRural population methodology is defined by various national statistical offices. In the United States, for example, the US Census Bureau's urban-rural classification is fundamentally a delineation of geographical areas, identifying both individual urban areas and the rural areas of the nation. \"Rural\" encompasses all population, housing, and territory not included within an urban area.\n\n### Limitations and exceptions:\nAggregation of urban and rural population may not add up to total population because of different country coverage.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution. To estimate urban populations, UN ratios of urban to total population were applied to the World Bank's estimates of total population.", "origins": [ { "producer": "World Bank based on data from the UN Population Division", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions. Indicator SP.RUR.TOTL.ZS (https://data.worldbank.org/indicator/SP.RUR.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.RUR.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total population" } }, "sp_urb_grow": { "title": "Urban population growth (annual %)", "description_from_producer": "Annual urban population growth rate for year t is the exponential rate of growth of midyear urban population from year t-1 to t, expressed as a percentage. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank total population estimates and urban ratios from the United Nations World Urbanization Prospects.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated from urban population estimates. The urban population estimates are calculated using World Bank's total population estimates and urban ratios from the United Nations World Urbanization Prospects.\n\n\n\n\n\n\n\n\n\n\n\nThe growth rate is computed using the exponential growth formula:\n\n\n\n\n\n\n\n\n\n\n\nr = ln(pn/p0)/n,\n\n\n\n\n\n\n\n\n\n\n\nwhere r is the exponential rate of growth, ln() is the natural logarithm, pn is the end period population, p0 is the beginning period population, and n is the number of years in between. Note that this is not the geometric growth rate used to compute compound growth over discrete periods.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen.\n\n### Development relevance:\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment.\n\n### Limitations and exceptions:\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution.", "origins": [ { "producer": "World Bank based on data from the UN Population Division", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions. Indicator SP.URB.GROW (https://data.worldbank.org/indicator/SP.URB.GROW). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.URB.GROW", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "annual %", "short_unit": "%", "display": { "shortUnit": "%", "unit": "annual %" } }, "sp_urb_totl": { "title": "Urban population", "description_from_producer": "Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The indicator is calculated using World Bank's total population estimates and urban ratios from the United Nations World Urbanization Prospects.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen.\n\n### Development relevance:\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment.\n\n### Limitations and exceptions:\nAggregation of urban and rural population may not add up to total population because of different country coverage.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution.", "origins": [ { "producer": "World Bank based on data from the UN Population Division", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division;\nStaff estimates, World Bank (WB), note: World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects, National definitions. Indicator SP.URB.TOTL (https://data.worldbank.org/indicator/SP.URB.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.URB.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "sp_urb_totl_in_zs": { "title": "Urban population (% of total population)", "description_from_producer": "Urban population refers to people living in urban areas as defined by national statistical offices. The data are collected and smoothed by United Nations Population Division.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Percentages urban are the numbers of persons residing in an area defined as ''urban'' per 100 total population.\nStatistical concept(s): Urban population refers to people living in urban areas as defined by national statistical offices. Particular caution should be used in interpreting the figures for percentage urban for different countries. Countries differ in the way they classify population as \"urban\" or \"rural.\" The population of a city or metropolitan area depends on the boundaries chosen.\n\n### Development relevance:\nExplosive growth of cities globally signifies the demographic transition from rural to urban, and is associated with shifts from an agriculture-based economy to mass industry, technology, and service.\n\n\n\n\n\n\n\n\n\n\n\nIn principle, cities offer a more favorable setting for the resolution of social and environmental problems than rural areas. Cities generate jobs and income, and deliver education, health care and other services. Cities also present opportunities for social mobilization and women's empowerment.\n\n### Limitations and exceptions:\nAggregation of urban and rural population may not add up to total population because of different country coverage.\n\nMost countries use an urban classification related to the size or characteristics of settlements. Some define urban areas based on the presence of certain infrastructure and services. And other countries designate urban areas based on administrative arrangements. Because of national differences in the characteristics that distinguish urban from rural areas, the distinction between urban and rural population is not amenable to a single definition that would be applicable to all countries.\n\nEstimates of the world's urban population would change significantly if China, India, and a few other populous nations were to change their definition of urban centers.\n\nBecause the estimates of city and metropolitan area are based on national definitions of what constitutes a city or metropolitan area, cross-country comparisons should be made with caution.", "origins": [ { "producer": "World Urbanization Prospects - UN Population Division, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Urbanization Prospects, United Nations (UN), uri: https://population.un.org/wup/, note: United Nations Population Division's World Urbanization Prospects, National definitions, publisher: UN Population Division. Indicator SP.URB.TOTL.IN.ZS (https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total" } }, "sp_uwt_tfrt": { "title": "Unmet need for contraception (% of married women ages 15-49)", "description_from_producer": "Unmet need for contraception is the percentage of fertile, married women of reproductive age who do not want to become pregnant and are not using contraception.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.\n\n\n\nMany couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects. This indicator excludes women not exposed to the risk of unintended pregnancy because of menopause, infertility, or postpartum anovulation.\n\n### Other notes:\nUnmet need for contraception measures the capacity women have in achieving their desired family size and birth spacing. Many couples in developing countries want to limit or postpone childbearing but are not using effective contraception. These couples have an unmet need for contraception. Common reasons are lack of knowledge about contraceptive methods and concerns about possible side effects.", "origins": [ { "producer": "Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), and UN Population Division, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Household surveys, United Nations (UN), note: Household surveys, including Demographic and Health Surveys and Multiple Indicator Cluster Surveys. Largely compiled by United Nations Population Division., publisher: UN Population Division. Indicator SP.UWT.TFRT (https://data.worldbank.org/indicator/SP.UWT.TFRT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.UWT.TFRT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of married women ages 15-49", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of married women ages 15-49" } }, "st_int_arvl": { "title": "International tourism, number of arrivals", "description_from_producer": "International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Statistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nArrivals data measure the flows of international visitors to the country of reference: each arrival corresponds to one in inbound tourism trip. If a person visits several countries during the course of a single trip, his/her arrival in each country is recorded separately. In an accounting period, arrivals are not necessarily equal to the number of persons travelling (when a person visits the same country several times a year, each trip by the same person is counted as a separate arrival).\n\nArrivals data should correspond to inbound visitors by including both tourists and same-day non-resident visitors. All other types of travelers (such as border, seasonal and other short-term workers, long-term students and others) should be excluded as they do not qualify as visitors.\n\nData are obtained from different sources: administrative records (immigration, traffic counts, and other possible types of controls), border surveys or a mix of them. If data are obtained from accommodation surveys, the number of guests is used as estimate of arrival figures; consequently, in this case, breakdowns by regions, main purpose of the trip, modes of transport used or forms of organization of the trip are based on complementary visitor surveys.\n\n### Development relevance:\nTourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.\n\n### Limitations and exceptions:\nTourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival. The data on inbound tourism show the arrivals of nonresident tourists (overnight visitors) at national borders. When data on international tourists are unavailable or incomplete, the data show the arrivals of international visitors, which include tourists, same-day visitors, cruise passengers, and crew members.\n\nSources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries.", "origins": [ { "producer": "UN World Tourism Organization (Yearbook of Tourism Statistics), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism. Indicator ST.INT.ARVL (https://data.worldbank.org/indicator/ST.INT.ARVL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ST.INT.ARVL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "st_int_dprt": { "title": "International tourism, number of departures", "description_from_producer": "International outbound tourists are the number of departures that people make from their country of usual residence to any other country for any purpose other than a remunerated activity in the country visited. The data on outbound tourists refer to the number of departures, not to the number of people traveling. Thus a person who makes several trips from a country during a given period is counted each time as a new departure.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Statistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nDepartures data measure the flows of resident visitors leaving the country of reference. Departures are not necessarily equal to the number of arrivals reported by international destinations for the country of reference.\n\nIn many countries, the characteristics of trips and visitors are established through questions on the entry/departure cards, in surveys at the borders, at destination (accommodation surveys) or as part of household surveys (for domestic and outbound tourism). The entry/departure cards, or records of entry and departure, captured and reconciled by the immigration authorities are often the basic source for establishing the flows of inbound and outbound visitors. These cards usually collect information on a census basis on name, sex, age, nationality, current address, date of arrival (or departure in the departure card), purpose of trip, main destination visited and length of stay (expected on arrival and actual on departure for inbound visitors; expected on departure and actual on arrival for outbound visitors).\n\nData is collected using one of three methods, or a combination of these to determine the flows of outbound visitors: using an entry/departure card; a specific survey at the border, or observing them from household surveys because they belong to resident households. In the latter case, the information on outbound trips is usually collected at the same time as that on domestic trips.\n\n### Development relevance:\nTourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.\n\n### Limitations and exceptions:\nTourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling.", "origins": [ { "producer": "UN World Tourism Organization (Yearbook of Tourism Statistics), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism. Indicator ST.INT.DPRT (https://data.worldbank.org/indicator/ST.INT.DPRT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ST.INT.DPRT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "" }, "st_int_rcpt_cd": { "title": "International tourism, receipts (current US$)", "description_from_producer": "International tourism receipts are expenditures by international inbound visitors, including payments to national carriers for international transport. These receipts include any other prepayment made for goods or services received in the destination country. They also may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Data are in current U.S. dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Inbound tourism expenditures may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Their share in exports is calculated as a ratio to exports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization (WTO) reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates.\n\n### Development relevance:\nTourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.\n\n### Limitations and exceptions:\nTourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival. The data on inbound tourism show the arrivals of nonresident tourists (overnight visitors) at national borders. When data on international tourists are unavailable or incomplete, the data show the arrivals of international visitors, which include tourists, same-day visitors, cruise passengers, and crew members.\n\nSources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP): in the case of inbound tourism, those expenditures associated with inbound visitors are registered as \"credits\" in the BOP and refers to \"travel receipts\".\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data.", "origins": [ { "producer": "UN World Tourism Organization (Yearbook of Tourism Statistics), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism. Indicator ST.INT.RCPT.CD (https://data.worldbank.org/indicator/ST.INT.RCPT.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ST.INT.RCPT.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "st_int_rcpt_xp_zs": { "title": "International tourism, receipts (% of total exports)", "description_from_producer": "International tourism receipts are expenditures by international inbound visitors, including payments to national carriers for international transport. These receipts include any other prepayment made for goods or services received in the destination country. They also may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Their share in exports is calculated as a ratio to exports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Inbound tourism expenditures may include receipts from same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include receipts for passenger transport items. Their share in exports is calculated as a ratio to exports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nInternational tourism expenditures' share in exports is calculated as a ratio to exports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services.\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization (WTO) reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates.\n\n### Development relevance:\nTourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.\n\nThis measure reflects the importance of tourism as an internationally traded service relative to other categories of exports. Such a measure reveals the degree of tourism specialization in a country's export structure and the relative capability of tourism in generating foreign revenues.\n\n### Limitations and exceptions:\nTourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival. The data on inbound tourism show the arrivals of nonresident tourists (overnight visitors) at national borders. When data on international tourists are unavailable or incomplete, the data show the arrivals of international visitors, which include tourists, same-day visitors, cruise passengers, and crew members.\n\nSources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP): in the case of inbound tourism, those expenditures associated with inbound visitors are registered as \"credits\" in the BOP and refers to \"travel receipts\".\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data.", "origins": [ { "producer": "UN World Tourism Organization, International Monetary Fund (IMF), and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism;\nIMF exports estimates, International Monetary Fund (IMF);\nWorld Bank exports estimates, World Bank (WB). Indicator ST.INT.RCPT.XP.ZS (https://data.worldbank.org/indicator/ST.INT.RCPT.XP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ST.INT.RCPT.XP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total exports" } }, "st_int_trnr_cd": { "title": "International tourism, receipts for passenger transport items (current US$)", "description_from_producer": "International tourism receipts for passenger transport items are expenditures by international inbound visitors for all services provided in the international transportation by resident carriers. Also included are passenger services performed within an economy by nonresident carriers. Excluded are passenger services provided to nonresidents by resident carriers within the resident economies; these are included in travel items. In addition to the services covered by passenger fares--including fares that are a part of package tours but excluding cruise fares, which are included in travel--passenger services include such items as charges for excess baggage, vehicles, or other personal accompanying effects and expenditures for food, drink, or other items for which passengers make expenditures while on board carriers. Data are in current U.S. dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown.", "origins": [ { "producer": "UN World Tourism Organization (Yearbook of Tourism Statistics), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism. Indicator ST.INT.TRNR.CD (https://data.worldbank.org/indicator/ST.INT.TRNR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ST.INT.TRNR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "st_int_trnx_cd": { "title": "International tourism, expenditures for passenger transport items (current US$)", "description_from_producer": "International tourism expenditures for passenger transport items are expenditures of international outbound visitors in other countries for all services provided during international transportation by nonresident carriers. Also included are passenger services performed within an economy by nonresident carriers. Excluded are passenger services provided to nonresidents by resident carriers within the resident economies; these are included in travel items. In addition to the services covered by passenger fares--including fares that are a part of package tours but excluding cruise fares, which are included in travel--passenger services include such items as charges for excess baggage, vehicles, or other personal accompanying effects and expenditures for food, drink, or other items for which passengers make expenditures while on board carriers. Data are in current U.S. dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown.", "origins": [ { "producer": "UN World Tourism Organization (Yearbook of Tourism Statistics), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism. Indicator ST.INT.TRNX.CD (https://data.worldbank.org/indicator/ST.INT.TRNX.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ST.INT.TRNX.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "st_int_tvlr_cd": { "title": "International tourism, receipts for travel items (current US$)", "description_from_producer": "International tourism receipts for travel items are expenditures by international inbound visitors in the reporting economy. The goods and services are purchased by, or on behalf of, the traveler or provided, without a quid pro quo, for the traveler to use or give away. These receipts should include any other prepayment made for goods or services received in the destination country. They also may include receipts from same-day visitors, except in cases where these are so important as to justify a separate classification. Excluded is the international carriage of travelers, which is covered in passenger travel items. Data are in current U.S. dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown.", "origins": [ { "producer": "UN World Tourism Organization (Yearbook of Tourism Statistics), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism. Indicator ST.INT.TVLR.CD (https://data.worldbank.org/indicator/ST.INT.TVLR.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ST.INT.TVLR.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "st_int_tvlx_cd": { "title": "International tourism, expenditures for travel items (current US$)", "description_from_producer": "International tourism expenditures are expenditures of international outbound visitors in other countries. The goods and services are purchased by, or on behalf of, the traveler or provided, without a quid pro quo, for the traveler to use or give away. These may include expenditures by residents traveling abroad as same-day visitors, except in cases where these are so important as to justify a separate classification. Excluded is the international carriage of travelers, which is covered in passenger travel items. Data are in current U.S. dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Tourism data is compiled based on the International Recommendations for Tourism Statistics (IRTS 2008). The World Tourism Organization enhances its coverage of tourism expenditure data by using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. When data on passenger transport items is unavailable, expenditure data for travel items are shown.", "origins": [ { "producer": "UN World Tourism Organization (Yearbook of Tourism Statistics), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism. Indicator ST.INT.TVLX.CD (https://data.worldbank.org/indicator/ST.INT.TVLX.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ST.INT.TVLX.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "st_int_xpnd_cd": { "title": "International tourism, expenditures (current US$)", "description_from_producer": "International tourism expenditures are expenditures of international outbound visitors in other countries, including payments to foreign carriers for international transport. These expenditures may include those by residents traveling abroad as same-day visitors, except in cases where these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Data are in current U.S. dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: Outbound tourism expenditures may include those by residents traveling abroad as same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Their share in imports is calculated as a ratio to imports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates.\n\n### Development relevance:\nTourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.\n\n### Limitations and exceptions:\nTourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP).\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data.", "origins": [ { "producer": "UN World Tourism Organization (Yearbook of Tourism Statistics), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism. Indicator ST.INT.XPND.CD (https://data.worldbank.org/indicator/ST.INT.XPND.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ST.INT.XPND.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "st_int_xpnd_mp_zs": { "title": "International tourism, expenditures (% of total imports)", "description_from_producer": "International tourism expenditures are expenditures of international outbound visitors in other countries, including payments to foreign carriers for international transport. These expenditures may include those by residents traveling abroad as same-day visitors, except in cases where these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Their share in imports is calculated as a ratio to imports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Outbound tourism expenditures may include those by residents traveling abroad as same-day visitors, except when these are important enough to justify separate classification. For some countries they do not include expenditures for passenger transport items. Their share in imports is calculated as a ratio to imports of goods and services (all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services).\n\nInternational tourism expenditures' share in imports is calculated as a ratio to imports of goods and services, which comprise all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, goods sent for processing and repairs, nonmonetary gold, and services.\n\nStatistical information on tourism is based mainly on data on arrivals and overnight stays along with balance of payments information. These data do not completely capture the economic phenomenon of tourism or provide the information needed for effective public policies and efficient business operations. Data are needed on the scale and significance of tourism. Information on the role of tourism in national economies is particularly deficient. Although the World Tourism Organization reports progress in harmonizing definitions and measurement, differences in national practices still prevent full comparability.\n\nThe World Tourism Organization is improving its coverage of tourism expenditure data, using balance of payments data from the International Monetary Fund (IMF) supplemented by data from individual countries. These data include travel and passenger transport items as defined in the IMF's Balance of Payments. When the IMF does not report data on passenger transport items, expenditure data for travel items are shown.\n\nThe aggregates are calculated using the World Bank's weighted aggregation methodology and differ from the World Tourism Organization's aggregates.\n\n### Development relevance:\nTourism is officially recognized as a directly measurable activity, enabling more accurate analysis and more effective policy. Whereas previously the sector relied mostly on approximations from related areas of measurement (e.g. Balance of Payments statistics), tourism today possesses a range of instruments to track its productive activities and the activities of the consumers that drive them: visitors (both tourists and excursionists).\n\nAn increasing number of countries have opened up and invested in tourism development, making tourism a key driver of socio-economic progress through export revenues, the creation of jobs and enterprises, and infrastructure development. As an internationally traded service, inbound tourism has become one of the world's major trade categories. For many developing countries it is one of the main sources of foreign exchange income and a major component of exports, creating much needed employment and development opportunities.\n\nThis measure reflects the importance of tourism as an internationally traded service relative to other categories of imports. Such a measure reveals the predilection for tourism in a country's import structure and the relative degree of an economy's domestic revenue outflows due to international tourism.\n\n### Limitations and exceptions:\nTourism can be either domestic or international. The data refers to international tourism, where the traveler's country of residence differs from the visiting country. International tourism consists of inbound (arrival) and outbound (departures) tourism.\n\nThe data are from the World Tourism Organization (WTO), a United Nations agency. The data on inbound and outbound tourists refer to the number of arrivals and departures, not to the number of people traveling.\n\nExpenditure associated with the activity of international visitors has been traditionally identified with the travel item of the Balance of Payments (BOP).\n\nThe 2008 International Recommendations for Tourism Statistics consider that \"tourism industries and products\" includes transport of passengers. Consequently, a better estimate of tourism-related expenditure by inbound and outbound visitors in an international scenario would be, in terms of the BOP, the value of the travel item plus that of the passenger transport item.\n\nNevertheless, users should be aware that BOP estimates include, in addition to expenditures associated to visitors, those related to other types of travelers (these might be substantial in some countries; for instance, long-term students or patients, border and seasonal workers, etc.). Also data on expenditure by main purpose of the trip are BOP data.", "origins": [ { "producer": "Yearbook of Tourism Statistics - UN Tourism, IMF, and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files, UN Tourism;\nIMF imports estimates, International Monetary Fund (IMF);\nWorld Bank imports estimates, World Bank (WB). Indicator ST.INT.XPND.MP.ZS (https://data.worldbank.org/indicator/ST.INT.XPND.MP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/ST.INT.XPND.MP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total imports" } }, "tg_val_totl_gd_zs": { "title": "Merchandise trade (% of GDP)", "description_from_producer": "General merchandise trade includes goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e. It is the total of merchandise exports plus merchandise imports. This indicator is expressed as a percentage of Gross Domestic Product (GDP) which is the total income earned through the production of goods and services in an economic territory during an accounting period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.", "origins": [ { "producer": "World Trade Organization (WTO) and World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Trade Organization (WTO);\nWorld Bank GDP estimates, World Bank (WB);\nWorld Development Indicators, World Bank (WB). Indicator TG.VAL.TOTL.GD.ZS (https://data.worldbank.org/indicator/TG.VAL.TOTL.GD.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TG.VAL.TOTL.GD.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "tm_qty_mrch_xd_wd": { "title": "Import volume index (2015 = 100)", "description_from_producer": "Import volume indexes are derived from UNCTAD's volume index series and are the ratio of the import value indexes to the corresponding unit value indexes. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year's trade values at the Standard International Trade Classification three-digit level as weights.\n\n### Statistical concept and methodology:\nMethodology: This indicator is constructed as the ratio of the import value index to the corresponding unit value index, both compiled by the United Nations Conference on Trade and Development (UNCTAD). The import value index reflects the nominal value of imports over time, while the unit value index serves as a proxy for import prices by capturing changes in average prices per unit. Unit value indexes are based primarily on data reported by national statistical authorities. To ensure data reliability, only values from countries that demonstrate internal consistency and meet quality thresholds defined by UNCTAD are used. Where data are incomplete or fail to meet these standards, UNCTAD supplements them with its own estimates.\n\nThe estimation procedure for unit value indexes involves applying trade values from the previous year as weights, using the Standard International Trade Classification (SITC) at the three-digit level of aggregation. This method helps to reduce distortions from shifts in trade structure or classification inconsistencies. The resulting indexes are chain-linked to ensure temporal comparability and are rebased to the year 2015 (2015 = 100). The final import volume index allows for the analysis of changes in the physical volume of imports over time, net of price effects, and is particularly useful for assessing trends in trade flows in real terms.", "origins": [ { "producer": "United Nations Conference on Trade and Development (UNCTAD), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Conference on Trade and Development (UNCTAD). Indicator TM.QTY.MRCH.XD.WD (https://data.worldbank.org/indicator/TM.QTY.MRCH.XD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.QTY.MRCH.XD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2015 = 100", "display": { "unit": "2015 = 100" } }, "tm_tax_manf_bc_zs": { "title": "Binding coverage, manufactured products (%)", "description_from_producer": "Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68.\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated as the proportion of tariff lines for manufactured products that are bound under World Trade Organization (WTO) commitments. A tariff line is considered bound if a maximum rate is legally committed in the WTO schedule of concessions. The binding coverage is computed by dividing the number of bound tariff lines by the total number of tariff lines in the relevant product category and multiplying the result by 100 to express it as a percentage. Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator is compiled using only officially reported bound statuses without imputation. It is methodologically consistent across countries, with tariff line concordance procedures applied to standardize classification and maintain comparability across HS revisions.", "origins": [ { "producer": "World Bank staff estimates, WITS, and WTO", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO). Indicator TM.TAX.MANF.BC.ZS (https://data.worldbank.org/indicator/TM.TAX.MANF.BC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MANF.BC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_manf_br_zs": { "title": "Bound rate, simple mean, manufactured products (%)", "description_from_producer": "Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68.\n\n### Statistical concept and methodology:\nMethodology: This indicator represents the unweighted average of final bound tariff rates across all manufactured products tariff lines. The calculation involves summing the bound rates applied to each individual tariff line and dividing by the total number of lines within the product category. Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Manufactured products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator reflects commitments under WTO agreements and is not adjusted for trade volume. It includes only those lines for which binding rates have been reported. The methodology ensures consistency across countries by converting national tariff submissions to a harmonized HS version.", "origins": [ { "producer": "World Bank staff estimates, WITS, and WTO", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO). Indicator TM.TAX.MANF.BR.ZS (https://data.worldbank.org/indicator/TM.TAX.MANF.BR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MANF.BR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_manf_sm_ar_zs": { "title": "Tariff rate, applied, simple mean, manufactured products (%)", "description_from_producer": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68.\n\n### Statistical concept and methodology:\nMethodology: This indicator represents the simple (unweighted) average of applied most-favored-nation (MFN) tariff rates across all tariff lines within the manufactured products category. Each line is given equal weight regardless of the trade volume associated with it. The mean is calculated by summing the applied rates and dividing by the number of lines. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator excludes missing or unreported lines. Harmonization across HS revisions is conducted to ensure international comparability, and no adjustments are made beyond validation of submitted data.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.MANF.SM.AR.ZS (https://data.worldbank.org/indicator/TM.TAX.MANF.SM.AR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MANF.SM.AR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_manf_sm_fn_zs": { "title": "Tariff rate, most favored nation, simple mean, manufactured products (%)", "description_from_producer": "Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68.\n\n### Statistical concept and methodology:\nMethodology: This indicator calculates the simple (unweighted) mean of statutory most-favored-nation (MFN) tariff rates across all tariff lines for manufactured products. It reflects tariff rates that apply to all World Trade Organization (WTO) members on a non-discriminatory basis unless preferential agreements are in force. Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The calculation is performed by summing the MFN rates and dividing by the total number of tariff lines. No trade-based weighting is applied. Only reported rates are included, and harmonized classifications ensure comparability across countries.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.MANF.SM.FN.ZS (https://data.worldbank.org/indicator/TM.TAX.MANF.SM.FN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MANF.SM.FN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_manf_wm_ar_zs": { "title": "Tariff rate, applied, weighted mean, manufactured products (%)", "description_from_producer": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68.\n\n### Statistical concept and methodology:\nMethodology: This indicator computes the average of applied most-favored-nation (MFN) tariff rates for manufactured products, using import values as weights. Tariff lines with higher trade volumes have a proportionally greater influence on the final average. The weighted mean is derived by multiplying each tariff rate by its corresponding import value, summing these products, and dividing by the total import value. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Trade data used for weighting are obtained from official national or international sources. Only lines with both valid tariff rates and trade values are included. Harmonized classifications are used to align trade and tariff data consistently.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.MANF.WM.AR.ZS (https://data.worldbank.org/indicator/TM.TAX.MANF.WM.AR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MANF.WM.AR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_manf_wm_fn_zs": { "title": "Tariff rate, most favored nation, weighted mean, manufactured products (%)", "description_from_producer": "Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68.\n\n### Statistical concept and methodology:\nMethodology: This indicator represents the trade-weighted average of most-favored-nation (MFN) tariff rates across all manufactured products tariff lines. Import values are used to weight each rate, reflecting the relative economic significance of different products in international trade. The weighted average is calculated by taking the sum of the products of MFN rates and import values, divided by the total import value. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Manufactured products are commodities classified in SITC revision 3 sections 5-8 excluding division 68. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only tariff lines with both reported MFN rates and corresponding trade values are included. The WTO applies standardized concordance procedures to match trade and tariff data using harmonized product classifications.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.MANF.WM.FN.ZS (https://data.worldbank.org/indicator/TM.TAX.MANF.WM.FN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MANF.WM.FN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_mrch_bc_zs": { "title": "Binding coverage, all products (%)", "description_from_producer": "Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable.\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated as the proportion of tariff lines for all products that are bound under World Trade Organization (WTO) commitments. A tariff line is considered bound if a maximum rate is legally committed in the WTO schedule of concessions. The binding coverage is computed by dividing the number of bound tariff lines by the total number of tariff lines in the relevant product category and multiplying the result by 100 to express it as a percentage. Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator is compiled using only officially reported bound statuses without imputation. It is methodologically consistent across countries, with tariff line concordance procedures applied to standardize classification and maintain comparability across HS revisions.", "origins": [ { "producer": "World Bank staff estimates, WITS, and WTO", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO). Indicator TM.TAX.MRCH.BC.ZS (https://data.worldbank.org/indicator/TM.TAX.MRCH.BC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MRCH.BC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_mrch_br_zs": { "title": "Bound rate, simple mean, all products (%)", "description_from_producer": "Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable.\n\n### Statistical concept and methodology:\nMethodology: This indicator represents the unweighted average of final bound tariff rates across all all products tariff lines. The calculation involves summing the bound rates applied to each individual tariff line and dividing by the total number of lines within the product category. Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator reflects commitments under WTO agreements and is not adjusted for trade volume. It includes only those lines for which binding rates have been reported. The methodology ensures consistency across countries by converting national tariff submissions to a harmonized HS version.", "origins": [ { "producer": "World Bank staff estimates, WITS, and WTO", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO). Indicator TM.TAX.MRCH.BR.ZS (https://data.worldbank.org/indicator/TM.TAX.MRCH.BR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MRCH.BR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_mrch_sm_ar_zs": { "title": "Tariff rate, applied, simple mean, all products (%)", "description_from_producer": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs.\n\n### Statistical concept and methodology:\nMethodology: This indicator represents the simple (unweighted) average of applied most-favored-nation (MFN) tariff rates across all tariff lines within the all products category. Each line is given equal weight regardless of the trade volume associated with it. The mean is calculated by summing the applied rates and dividing by the number of lines. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator excludes missing or unreported lines. Harmonization across HS revisions is conducted to ensure international comparability, and no adjustments are made beyond validation of submitted data.\n\n### Development relevance:\nPoor people in developing countries work primarily in agriculture and labor-intensive manufactures, sectors that confront the greatest trade barriers. Removing barriers to merchandise trade could increase growth in these countries - even more if trade in services were also liberalized.\n\nIn general, tariffs in high-income countries on imports from developing countries, though low, are twice those collected from other high-income countries. But protection is also an issue for developing countries, which maintain high tariffs on agricultural commodities, labor-intensive manufactures, and other products and services.\n\nCountries use a combination of tariff and nontariff measures to regulate imports. The most common form of tariff is an ad valorem duty, based on the value of the import, but tariffs may also be levied on a specific, or per unit, basis or may combine ad valorem and specific rates. Tariffs may be used to raise fiscal revenues or to protect domestic industries from foreign competition - or both. Nontariff barriers, which limit the quantity of imports of a particular good, include quotas, prohibitions, licensing schemes, export restraint arrangements, and health and quarantine measures. Because of the difficulty of combining nontariff barriers into an aggregate indicator, they are not included in the data.\n\nSome countries set fairly uniform tariff rates across all imports. Others are selective, setting high tariffs to protect favored domestic industries. The effective rate of protection - the degree to which the value added in an industry is protected - may exceed the nominal rate if the tariff system systematically differentiates among imports of raw materials, intermediate products, and finished goods.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.MRCH.SM.AR.ZS (https://data.worldbank.org/indicator/TM.TAX.MRCH.SM.AR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MRCH.SM.AR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_mrch_sm_fn_zs": { "title": "Tariff rate, most favored nation, simple mean, all products (%)", "description_from_producer": "Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups.\n\n### Statistical concept and methodology:\nMethodology: This indicator calculates the simple (unweighted) mean of statutory most-favored-nation (MFN) tariff rates across all tariff lines for all products. It reflects tariff rates that apply to all World Trade Organization (WTO) members on a non-discriminatory basis unless preferential agreements are in force. Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The calculation is performed by summing the MFN rates and dividing by the total number of tariff lines. No trade-based weighting is applied. Only reported rates are included, and harmonized classifications ensure comparability across countries.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.MRCH.SM.FN.ZS (https://data.worldbank.org/indicator/TM.TAX.MRCH.SM.FN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MRCH.SM.FN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_mrch_wm_ar_zs": { "title": "Tariff rate, applied, weighted mean, all products (%)", "description_from_producer": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead.\n\n### Statistical concept and methodology:\nMethodology: This indicator computes the average of applied most-favored-nation (MFN) tariff rates for all products, using import values as weights. Tariff lines with higher trade volumes have a proportionally greater influence on the final average. The weighted mean is derived by multiplying each tariff rate by its corresponding import value, summing these products, and dividing by the total import value. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Trade data used for weighting are obtained from official national or international sources. Only lines with both valid tariff rates and trade values are included. Harmonized classifications are used to align trade and tariff data consistently.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.MRCH.WM.AR.ZS (https://data.worldbank.org/indicator/TM.TAX.MRCH.WM.AR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MRCH.WM.AR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_mrch_wm_fn_zs": { "title": "Tariff rate, most favored nation, weighted mean, all products (%)", "description_from_producer": "Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database.\n\n### Statistical concept and methodology:\nMethodology: This indicator represents the trade-weighted average of most-favored-nation (MFN) tariff rates across all products tariff lines. Import values are used to weight each rate, reflecting the relative economic significance of different products in international trade. The weighted average is calculated by taking the sum of the products of MFN rates and import values, divided by the total import value. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only tariff lines with both reported MFN rates and corresponding trade values are included. The WTO applies standardized concordance procedures to match trade and tariff data using harmonized product classifications.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.MRCH.WM.FN.ZS (https://data.worldbank.org/indicator/TM.TAX.MRCH.WM.FN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.MRCH.WM.FN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_tcom_bc_zs": { "title": "Binding coverage, primary products (%)", "description_from_producer": "Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).\n\n### Statistical concept and methodology:\nMethodology: This indicator is calculated as the proportion of tariff lines for primary products that are bound under World Trade Organization (WTO) commitments. A tariff line is considered bound if a maximum rate is legally committed in the WTO schedule of concessions. The binding coverage is computed by dividing the number of bound tariff lines by the total number of tariff lines in the relevant product category and multiplying the result by 100 to express it as a percentage. Binding coverage is the percentage of product lines with an agreed bound rate. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator is compiled using only officially reported bound statuses without imputation. It is methodologically consistent across countries, with tariff line concordance procedures applied to standardize classification and maintain comparability across HS revisions.", "origins": [ { "producer": "World Bank staff estimates, WITS, and WTO", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO). Indicator TM.TAX.TCOM.BC.ZS (https://data.worldbank.org/indicator/TM.TAX.TCOM.BC.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.TCOM.BC.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_tcom_br_zs": { "title": "Bound rate, simple mean, primary products (%)", "description_from_producer": "Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).\n\n### Statistical concept and methodology:\nMethodology: This indicator represents the unweighted average of final bound tariff rates across all primary products tariff lines. The calculation involves summing the bound rates applied to each individual tariff line and dividing by the total number of lines within the product category. Simple mean bound rate is the unweighted average of all the lines in the tariff schedule in which bound rates have been set. Bound rates result from trade negotiations incorporated into a country's schedule of concessions and are thus enforceable. Primary products are commodities classified in Standard International Trade Classification (SITC) revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator reflects commitments under WTO agreements and is not adjusted for trade volume. It includes only those lines for which binding rates have been reported. The methodology ensures consistency across countries by converting national tariff submissions to a harmonized HS version.", "origins": [ { "producer": "World Bank staff estimates, WITS, and WTO", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nWorld Trade Organization (WTO). Indicator TM.TAX.TCOM.BR.ZS (https://data.worldbank.org/indicator/TM.TAX.TCOM.BR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.TCOM.BR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_tcom_sm_ar_zs": { "title": "Tariff rate, applied, simple mean, primary products (%)", "description_from_producer": "Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).\n\n### Statistical concept and methodology:\nMethodology: This indicator represents the simple (unweighted) average of applied most-favored-nation (MFN) tariff rates across all tariff lines within the primary products category. Each line is given equal weight regardless of the trade volume associated with it. The mean is calculated by summing the applied rates and dividing by the number of lines. Simple mean applied tariff is the unweighted average of effectively applied rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of simple mean tariffs. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The indicator excludes missing or unreported lines. Harmonization across HS revisions is conducted to ensure international comparability, and no adjustments are made beyond validation of submitted data.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.TCOM.SM.AR.ZS (https://data.worldbank.org/indicator/TM.TAX.TCOM.SM.AR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.TCOM.SM.AR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_tcom_sm_fn_zs": { "title": "Tariff rate, most favored nation, simple mean, primary products (%)", "description_from_producer": "Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).\n\n### Statistical concept and methodology:\nMethodology: This indicator calculates the simple (unweighted) mean of statutory most-favored-nation (MFN) tariff rates across all tariff lines for primary products. It reflects tariff rates that apply to all World Trade Organization (WTO) members on a non-discriminatory basis unless preferential agreements are in force. Simple mean most favored nation tariff rate is the unweighted average of most favored nation rates for all products subject to tariffs calculated for all traded goods. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (WTO) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. The calculation is performed by summing the MFN rates and dividing by the total number of tariff lines. No trade-based weighting is applied. Only reported rates are included, and harmonized classifications ensure comparability across countries.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.TCOM.SM.FN.ZS (https://data.worldbank.org/indicator/TM.TAX.TCOM.SM.FN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.TCOM.SM.FN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_tcom_wm_ar_zs": { "title": "Tariff rate, applied, weighted mean, primary products (%)", "description_from_producer": "Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).\n\n### Statistical concept and methodology:\nMethodology: This indicator computes the average of applied most-favored-nation (MFN) tariff rates for primary products, using import values as weights. Tariff lines with higher trade volumes have a proportionally greater influence on the final average. The weighted mean is derived by multiplying each tariff rate by its corresponding import value, summing these products, and dividing by the total import value. Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Trade data used for weighting are obtained from official national or international sources. Only lines with both valid tariff rates and trade values are included. Harmonized classifications are used to align trade and tariff data consistently.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.TCOM.WM.AR.ZS (https://data.worldbank.org/indicator/TM.TAX.TCOM.WM.AR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.TCOM.WM.AR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_tax_tcom_wm_fn_zs": { "title": "Tariff rate, most favored nation, weighted mean, primary products (%)", "description_from_producer": "Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).\n\n### Statistical concept and methodology:\nMethodology: This indicator represents the trade-weighted average of most-favored-nation (MFN) tariff rates across all primary products tariff lines. Import values are used to weight each rate, reflecting the relative economic significance of different products in international trade. The weighted average is calculated by taking the sum of the products of MFN rates and import values, divided by the total import value. Weighted mean most favored nations tariff is the average of most favored nation rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals). Tariff data are primarily sourced from the World Trade Organization (World Trade Organization (WTO)) Consolidated Tariff Schedules (Consolidated Tariff Schedules (CTS)) and Integrated Database (Integrated Database (IDB)), with supplementary inputs from the United Nations Conference on Trade and Development (United Nations Conference on Trade and Development (UNCTAD)). Data are submitted by national authorities using the Harmonized System (Harmonized System (HS)) classification. The WTO Secretariat validates and harmonizes submissions to a common HS version for comparability. Data are generally updated annually or as new schedules are submitted. Due to validation and alignment processes, there is typically a one- to two-year time lag between national reporting and publication. Only tariff lines with both reported MFN rates and corresponding trade values are included. The WTO applies standardized concordance procedures to match trade and tariff data using harmonized product classifications.", "origins": [ { "producer": "World Bank (WITS), UNCTAD (TRAINS), and World Trade Organization (WTO)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates;\nWorld Integrated Trade Solution system (WITS), World Bank (WB);\nTrade Analysis and Information System (TRAINS), UN Conference on Trade and Development (UNCTAD);\nIntegrated Data Base (IDB), World Trade Organization (WTO);\nConsolidated Tariff Schedules (CTS), World Trade Organization (WTO). Indicator TM.TAX.TCOM.WM.FN.ZS (https://data.worldbank.org/indicator/TM.TAX.TCOM.WM.FN.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.TAX.TCOM.WM.FN.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" } }, "tm_uvi_mrch_xd_wd": { "title": "Import unit value index (2015 = 100)", "description_from_producer": "Import unit value indices come from UNCTAD's trade database. Unit value indices are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD\u2019s estimates using the previous year\u2019s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD\u2019s Commodity Price Statistics, international and national sources, and UNCTAD secretariat estimates. This indicator is an index series where 2015=100.\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.", "origins": [ { "producer": "UN Conference on Trade and Development (UNCTAD), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Handbook of Statistics and data files., UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org. Indicator TM.UVI.MRCH.XD.WD (https://data.worldbank.org/indicator/TM.UVI.MRCH.XD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.UVI.MRCH.XD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2015 = 100", "display": { "unit": "2015 = 100" } }, "tm_val_agri_zs_un": { "title": "Agricultural raw materials imports (% of merchandise imports)", "description_from_producer": "Agricultural raw materials comprise section 2 of SITC Rev. 3 (crude materials, inedible, except fuels) excluding divisions 22 (oil-seeds and oleaginous fruits), 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nPrevious editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another.\n\n### Other notes:\nMerchandise import shares may not sum to 100 percent because of unclassified trade.", "origins": [ { "producer": "UN Comtrade, WITS, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB). Indicator TM.VAL.AGRI.ZS.UN (https://data.worldbank.org/indicator/TM.VAL.AGRI.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.AGRI.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of merchandise imports" } }, "tm_val_food_zs_un": { "title": "Food imports (% of merchandise imports)", "description_from_producer": "Food comprises the commodities in SITC (Rev. 3) sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and division 22 (oil seeds, oil nuts, and oil kernels). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nPrevious editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another.\n\n### Other notes:\nMerchandise import shares may not sum to 100 percent because of unclassified trade.", "origins": [ { "producer": "UN Comtrade, WITS, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB). Indicator TM.VAL.FOOD.ZS.UN (https://data.worldbank.org/indicator/TM.VAL.FOOD.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.FOOD.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of merchandise imports" } }, "tm_val_fuel_zs_un": { "title": "Fuel imports (% of merchandise imports)", "description_from_producer": "Fuels comprise the commodities in SITC (Rev. 3) section 3 (mineral fuels, lubricants and related materials). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nPrevious editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another.\n\n### Other notes:\nMerchandise import shares may not sum to 100 percent because of unclassified trade.", "origins": [ { "producer": "UN Comtrade, WITS, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB). Indicator TM.VAL.FUEL.ZS.UN (https://data.worldbank.org/indicator/TM.VAL.FUEL.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.FUEL.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of merchandise imports" } }, "tm_val_ictg_zs_un": { "title": "ICT goods imports (% total goods imports)", "description_from_producer": "Information and communication technology goods imports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Information and communication technology goods imports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous). Software is generally excluded, as there is a preference to record it under services (not an ICT good but an ICT product) to the extent possible. However it is hard to completely exclude embedded software from certain types of ICT goods, such as video game consoles (see for example the discussion on page 30 of the OECD guide cited below). ICT goods imports as a percentage of total imports is calculated for each country by dividing the value of its ICT goods imports by the total value of its goods imports. The result is then multiplied by 100 to be expressed as a percentage.\n\nICT goods are defined according to the OECD\u2019s Guide on Measuring the Information Society 2011 for Harmonized System (HS) 2007 and adapted to HS12 by UNCTAD in collaboration with UNSD (United Nations Statistics Division). This new list consists of 93 goods defined at the 6 digit level of the 2012 version of the HS. The technical note is available online at: http://unctad.org/en/PublicationsLibrary/tn_unctad_ict4d02_en.pdf\nData were downloaded from COMTRADE according to the reported classification (HS92, 96, 02, 07, 12) and aggregated into ICT groups by UNCTAD.\n\n### Development relevance:\nThe digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. For more information see www.itu.int/ITU-D/ict/partnership/.\n\nThe work of the Partnership is directed towards achieving internationally comparable and reliable ICT statistics. In order to achieve this, its members are involved in developing and maintaining a core list of ICT indicators. Other activities include the compilation and dissemination of ICT data, and the provision of technical assistance enabling statistical agencies to collect data that underlie the core list of ICT indicators.\n\n### Limitations and exceptions:\nDetailed trade data are widely available from country trade statistics. These are collected by the UNSD and published in their UN COMTRADE database. The ICT goods trade indicators are usually compiled by interested international and national agencies using COMTRADE data. Concepts are therefore consistent with those applying to the COMTRADE database.\n\nThe main statistical issue associated with this indicator appears to be the different treatment of re-exports and re-imports by countries, depending on whether the Special or General Trade System is used.2 Re-imports are separately reported for some countries and the value of ICT re-imports (which is included in the value of ICT imports for those countries) is generally small.", "origins": [ { "producer": "UNCTADstat - UNCTAD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNCTADstat database, UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org/ReportFolders/reportFolders.aspx. Indicator TM.VAL.ICTG.ZS.UN (https://data.worldbank.org/indicator/TM.VAL.ICTG.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.ICTG.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% total goods imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% total goods imports" } }, "tm_val_insf_zs_wt": { "title": "Insurance and financial services (% of commercial service imports)", "description_from_producer": "Financial services covers services related to financial intermediation, financial risk management, liquidity transformation or auxiliary financial activities. It also includes insurance and pension scheme services which are services related to providing life insurance and annuities, non-life insurance, reinsurance, pensions, standardised guarantees and auxiliary services to insurance, pension schemes, and standardised guarantee schemes. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nBalance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator TM.VAL.INSF.ZS.WT (https://data.worldbank.org/indicator/TM.VAL.INSF.ZS.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.INSF.ZS.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of commercial service imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of commercial service imports" } }, "tm_val_manf_zs_un": { "title": "Manufactures imports (% of merchandise imports)", "description_from_producer": "Manufactures comprise commodities in SITC (Rev. 3) sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous manufactured goods), excluding division 68 (non-ferrous metals). This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nPrevious editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another.\n\n### Other notes:\nMerchandise import shares may not sum to 100 percent because of unclassified trade.", "origins": [ { "producer": "UN Comtrade, WITS, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB). Indicator TM.VAL.MANF.ZS.UN (https://data.worldbank.org/indicator/TM.VAL.MANF.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MANF.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of merchandise imports" } }, "tm_val_mmtl_zs_un": { "title": "Ores and metals imports (% of merchandise imports)", "description_from_producer": "Ores and metals comprise the commodities in SITC (Rev. 3) sections 27 (crude fertilizer, minerals nes); 28 (metalliferous ores, scrap); and 68 (non-ferrous metals). Imports of services are services provided by non-residents to residents. This indicator is expressed as a percentage of merchandise imports which is comprised of goods whose economic ownership is changed between a non-resident and a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nPrevious editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another.\n\n### Other notes:\nMerchandise import shares may not sum to 100 percent because of unclassified trade.", "origins": [ { "producer": "UN Comtrade, WITS, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB). Indicator TM.VAL.MMTL.ZS.UN (https://data.worldbank.org/indicator/TM.VAL.MMTL.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MMTL.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of merchandise imports" } }, "tm_val_mrch_al_zs": { "title": "Merchandise imports from economies in the Arab World (% of total merchandise imports)", "description_from_producer": "Merchandise imports from economies in the Arab World are the sum of merchandise imports by the reporting economy from economies in the Arab World. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from economies in the Arab World. The purpose is to assess the relative importance of trade with economies in the Arab World economies within the broader context of global imports. The numerator includes the total value of goods imported from economies in the Arab World, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of economies in the Arab World follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.AL.ZS (https://data.worldbank.org/indicator/TM.VAL.MRCH.AL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.AL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise imports" } }, "tm_val_mrch_cd_wt": { "title": "Merchandise imports (current US$)", "description_from_producer": "Merchandise imports includes goods whose economic ownership is changed from a non-resident to a resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nThe value of imports is generally recorded as the cost of the goods when purchased by the importer plus the cost of transport and insurance to the frontier of the importing country - the cost, insurance, and freight (c.i.f.) value, corresponding to the landed cost at the point of entry of foreign goods into the country. A few countries collect import data on a free on board (f.o.b.) basis and adjust them for freight and insurance costs.\n\n\n\n\n\n\n\n\n\nCountries may report trade according to the general or special system of trade. Under the general system imports include goods imported for domestic consumption and imports into bonded warehouses and free trade zones. Under the special system imports comprise goods imported for domestic consumption (including transformation and repair) and withdrawals for domestic consumption from bonded warehouses and free trade zones. Goods transported through a country en route to another are excluded.\n\n\n\n\n\n\n\n\n\nData on imports of goods are derived from the same sources as data on exports. In principle, world exports and imports should be identical. Similarly, exports from an economy should equal the sum of imports by the rest of the world from that economy. But differences in timing and definitions result in discrepancies in reported values at all levels.", "origins": [ { "producer": "World Trade Organization (WTO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Trade Organization (WTO). Indicator TM.VAL.MRCH.CD.WT (https://data.worldbank.org/indicator/TM.VAL.MRCH.CD.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.CD.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "tm_val_mrch_hi_zs": { "title": "Merchandise imports from high-income economies (% of total merchandise imports)", "description_from_producer": "Merchandise imports from high-income economies are the sum of merchandise imports by the reporting economy from high-income economies according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from high-income economies. The purpose is to assess the relative importance of trade with high-income economies within the broader context of global imports. The numerator includes the total value of goods imported from high-income economies, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of high-income economies follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products.\n\n### Development relevance:\nLow- and middle-income economies are an increasingly important part of the global trading system. Trade between high-income economies and low- and middle-income economies has grown faster than trade between high-income economies. This increased trade benefits both producers and consumers in developing and high-income economies.\n\n### Limitations and exceptions:\nData on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.HI.ZS (https://data.worldbank.org/indicator/TM.VAL.MRCH.HI.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.HI.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise imports" } }, "tm_val_mrch_or_zs": { "title": "Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)", "description_from_producer": "Merchandise imports from low- and middle-income economies outside region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies outside region. The purpose is to assess the relative importance of trade with low- and middle-income economies outside region economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies outside region, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies outside region follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products.\n\n### Development relevance:\nAlthough global integration has increased, low- and middle-income economies still face trade barriers when accessing other markets.\n\n### Limitations and exceptions:\nData on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.OR.ZS (https://data.worldbank.org/indicator/TM.VAL.MRCH.OR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.OR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise imports" } }, "tm_val_mrch_r1_zs": { "title": "Merchandise imports from low- and middle-income economies in East Asia & Pacific (% of total merchandise imports)", "description_from_producer": "Merchandise imports from low- and middle-income economies in East Asia and Pacific are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the East Asia and Pacific region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in East Asia & Pacific. The purpose is to assess the relative importance of trade with low- and middle-income economies in East Asia & Pacific economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in East Asia & Pacific, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in East Asia & Pacific follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.R1.ZS (https://data.worldbank.org/indicator/TM.VAL.MRCH.R1.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.R1.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise imports" } }, "tm_val_mrch_r2_zs": { "title": "Merchandise imports from low- and middle-income economies in Europe & Central Asia (% of total merchandise imports)", "description_from_producer": "Merchandise imports from low- and middle-income economies in Europe and Central Asia are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Europe and Central Asia region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Europe & Central Asia. The purpose is to assess the relative importance of trade with low- and middle-income economies in Europe & Central Asia economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Europe & Central Asia, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Europe & Central Asia follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.R2.ZS (https://data.worldbank.org/indicator/TM.VAL.MRCH.R2.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.R2.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise imports" } }, "tm_val_mrch_r3_zs": { "title": "Merchandise imports from low- and middle-income economies in Latin America & the Caribbean (% of total merchandise imports)", "description_from_producer": "Merchandise imports from low- and middle-income economies in Latin America and the Caribbean are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Latin America and the Caribbean region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Latin America & the Caribbean. The purpose is to assess the relative importance of trade with low- and middle-income economies in Latin America & the Caribbean economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Latin America & the Caribbean, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Latin America & the Caribbean follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.R3.ZS (https://data.worldbank.org/indicator/TM.VAL.MRCH.R3.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.R3.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise imports" } }, "tm_val_mrch_r4_zs": { "title": "Merchandise imports from low- and middle-income economies in Middle East & North Africa (% of total merchandise imports)", "description_from_producer": "Merchandise imports from low- and middle-income economies in Middle East and North Africa are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Middle East and North Africa region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Middle East & North Africa. The purpose is to assess the relative importance of trade with low- and middle-income economies in Middle East & North Africa economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Middle East & North Africa, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Middle East & North Africa follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.R4.ZS (https://data.worldbank.org/indicator/TM.VAL.MRCH.R4.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.R4.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise imports" } }, "tm_val_mrch_r5_zs": { "title": "Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)", "description_from_producer": "Merchandise imports from low- and middle-income economies in South Asia are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the South Asia region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in South Asia. The purpose is to assess the relative importance of trade with low- and middle-income economies in South Asia economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in South Asia, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in South Asia follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.R5.ZS (https://data.worldbank.org/indicator/TM.VAL.MRCH.R5.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.R5.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise imports" } }, "tm_val_mrch_r6_zs": { "title": "Merchandise imports from low- and middle-income economies in Sub-Saharan Africa (% of total merchandise imports)", "description_from_producer": "Merchandise imports from low- and middle-income economies in Sub-Saharan Africa are the sum of merchandise imports by the reporting economy from low- and middle-income economies in the Sub-Saharan Africa region according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies in Sub-Saharan Africa. The purpose is to assess the relative importance of trade with low- and middle-income economies in Sub-Saharan Africa economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies in Sub-Saharan Africa, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies in Sub-Saharan Africa follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.R6.ZS (https://data.worldbank.org/indicator/TM.VAL.MRCH.R6.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.R6.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise imports" } }, "tm_val_mrch_rs_zs": { "title": "Merchandise imports by the reporting economy, residual (% of total merchandise imports)", "description_from_producer": "Merchandise imports by the reporting economy residuals are the total merchandise imports by the reporting economy from the rest of the world as reported in the IMF's Direction of trade database, less the sum of imports by the reporting economy from high-, low-, and middle-income economies according to the World Bank classification of economies. Includes trade with unspecified partners or with economies not covered by World Bank classification. Data are as a percentage of total merchandise imports by the economy.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator represents the total value of merchandise imports reported by a country over a specified period, typically a calendar year. The value includes all goods that physically enter the country\u2019s economic territory and are intended for consumption, processing, or re-export. Data are expressed in nominal terms using current U.S. dollars. The data source is primarily customs administrative records submitted by importers, compiled by national statistical or trade authorities.\n\nThe reported value of imports generally includes the cost of goods plus insurance and freight (CIF), which reflects the full landed value at the port of entry. The general trade system is used in most cases, meaning that imports into bonded warehouses and free zones are also included. The data exclude services and focus solely on physical goods crossing borders. Corrections may be applied for coverage limitations, underreporting, valuation discrepancies, or misclassifications to improve comparability.\n\nSome versions of the indicator may exclude re-imports, which are goods previously exported and returned to the country without significant transformation. This distinction ensures that the import figures do not overstate economic activity by counting the same goods more than once. Users of the indicator include government agencies monitoring trade flows, businesses analyzing sourcing strategies, and researchers studying trade balances. It also provides input for calculating the balance of trade and understanding integration into global markets.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.RS.ZS (https://data.worldbank.org/indicator/TM.VAL.MRCH.RS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.RS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise imports" } }, "tm_val_mrch_wl_cd": { "title": "Merchandise imports by the reporting economy (current US$)", "description_from_producer": "Merchandise imports by the reporting economy are the total merchandise imports by the reporting economy from the rest of the world, as reported in the IMF's Direction of trade database. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: This indicator represents the total value of merchandise imports reported by a country over a specified period, typically a calendar year. The value includes all goods that physically enter the country\u2019s economic territory and are intended for consumption, processing, or re-export. Data are expressed in nominal terms using current U.S. dollars. The data source is primarily customs administrative records submitted by importers, compiled by national statistical or trade authorities.\n\nThe reported value of imports generally includes the cost of goods plus insurance and freight (CIF), which reflects the full landed value at the port of entry. The general trade system is used in most cases, meaning that imports into bonded warehouses and free zones are also included. The data exclude services and focus solely on physical goods crossing borders. Corrections may be applied for coverage limitations, underreporting, valuation discrepancies, or misclassifications to improve comparability.\n\nSome versions of the indicator may exclude re-imports, which are goods previously exported and returned to the country without significant transformation. This distinction ensures that the import figures do not overstate economic activity by counting the same goods more than once. Users of the indicator include government agencies monitoring trade flows, businesses analyzing sourcing strategies, and researchers studying trade balances. It also provides input for calculating the balance of trade and understanding integration into global markets.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.WL.CD (https://data.worldbank.org/indicator/TM.VAL.MRCH.WL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.WL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "tm_val_mrch_wr_zs": { "title": "Merchandise imports from low- and middle-income economies within region (% of total merchandise imports)", "description_from_producer": "Merchandise imports from low- and middle-income economies within region are the sum of merchandise imports by the reporting economy from other low- and middle-income economies in the same World Bank region according to the World Bank classification of economies. Data are as a percentage of total merchandise imports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator measures the percentage of total merchandise imports by the reporting country that originate from low- and middle-income economies within region. The purpose is to assess the relative importance of trade with low- and middle-income economies within region economies within the broader context of global imports. The numerator includes the total value of goods imported from low- and middle-income economies within region, while the denominator includes all merchandise imports received by the reporting economy within the reference year. Both values are recorded in current U.S. dollars and typically sourced from national customs declarations.\n\nThe origin of merchandise imports is determined based on standard customs procedures, such as certificates of origin and supporting shipping documentation. Goods are generally recorded on a cost-insurance-freight (CIF) basis, meaning the import value includes the cost of the goods as well as transportation and insurance costs incurred up to the point of entry into the importing country. This ensures that comparisons across economies reflect the total landed value of imports.\n\nThe classification of low- and middle-income economies within region follows the grouping conventions available at the time of reporting. Where applicable, economies are grouped according to income level, regional affiliation, or development status. The indicator enables monitoring of trade exposure and diversification by identifying which country groupings supply a significant portion of merchandise goods. In some cases, governments may also use this metric to guide trade policy, tariff design, or regional cooperation strategies. Fluctuations in the share may reflect changes in trade agreements, exchange rates, supply chain dependencies, or global demand for specific products.\n\n### Development relevance:\nThe relative importance of intraregional trade is higher for both landlocked countries and small countries with close trade links to the largest regional economy. For most low- and middle-income economies - especially smaller ones - there is a \"geographic bias\" favoring intraregional trade. Despite the broad trend toward globalization and the reduction of trade barriers, the relative share of intraregional trade increased for most economies between 1999 and 2010. This is due partly to trade-related advantages, such as proximity, lower transport costs, increased knowledge from repeated interaction, and cultural and historical affinity. The direction of trade is also influenced by preferential trade agreements that a country has made with other economies. Though formal agreements on trade liberalization do not automatically increase trade, they nevertheless affect the direction of trade between the participating economies.\n\n### Limitations and exceptions:\nData on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TM.VAL.MRCH.WR.ZS (https://data.worldbank.org/indicator/TM.VAL.MRCH.WR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.WR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise imports" } }, "tm_val_mrch_xd_wd": { "title": "Import value index (2015 = 100)", "description_from_producer": "Import value indexes are the current value of imports (c.i.f.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's import value indexes are reported for most economies.\n\n### Statistical concept and methodology:\nMethodology: The Import Value Index (base year 2015 = 100) captures the movement in the total value of merchandise imports over time, using 2015 as the reference period. This index reflects changes in both the price and quantity of imported goods. An index value above 100 indicates that the total value of imports has increased compared to the base year, while a value below 100 suggests a decline. The indicator is constructed using current-dollar import values, typically sourced from customs data or trade statistics reports.\n\nImport values are aggregated across all products and trading partners. Because the index combines both price and volume effects, it does not isolate whether changes are due to rising prices, increased quantities, or both. For this reason, it is often interpreted alongside other indicators such as unit value or volume indexes.\n\nIndex values are calculated by comparing the nominal value of imports in each year to that of the base year, then scaling the result so that the base year equals 100. This normalization allows for straightforward interpretation and cross-country comparisons. The index is useful for identifying structural shifts in import demand, the impact of currency fluctuations, and the effects of trade policy. It can also be used in macroeconomic modeling to estimate import deflators or as an input for terms-of-trade calculations.", "origins": [ { "producer": "United Nations Conference on Trade and Development (UNCTAD), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Conference on Trade and Development (UNCTAD). Indicator TM.VAL.MRCH.XD.WD (https://data.worldbank.org/indicator/TM.VAL.MRCH.XD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.MRCH.XD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2015 = 100", "display": { "unit": "2015 = 100" } }, "tm_val_othr_zs_wt": { "title": "Computer, communications and other services (% of commercial service imports)", "description_from_producer": "Computer, communications and other services include such activities as international telecommunications, and postal and courier services; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recreational services. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nBalance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator TM.VAL.OTHR.ZS.WT (https://data.worldbank.org/indicator/TM.VAL.OTHR.ZS.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.OTHR.ZS.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of commercial service imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of commercial service imports" } }, "tm_val_serv_cd_wt": { "title": "Commercial service imports (current US$)", "description_from_producer": "Commercial service imports are total service imports minus imports of government services not included elsewhere. Imports of services are services provided by non-residents to residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nBalance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator TM.VAL.SERV.CD.WT (https://data.worldbank.org/indicator/TM.VAL.SERV.CD.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.SERV.CD.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "tm_val_tran_zs_wt": { "title": "Transport services (% of commercial service imports)", "description_from_producer": "Transport is the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nBalance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator TM.VAL.TRAN.ZS.WT (https://data.worldbank.org/indicator/TM.VAL.TRAN.ZS.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.TRAN.ZS.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of commercial service imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of commercial service imports" } }, "tm_val_trvl_zs_wt": { "title": "Travel services (% of commercial service imports)", "description_from_producer": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service imports which are commercial services provided by non-residents to residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nBalance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator TM.VAL.TRVL.ZS.WT (https://data.worldbank.org/indicator/TM.VAL.TRVL.ZS.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TM.VAL.TRVL.ZS.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of commercial service imports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of commercial service imports" } }, "tt_pri_mrch_xd_wd": { "title": "Net barter terms of trade index (2015 = 100)", "description_from_producer": "Net barter terms of trade index is calculated as the percentage ratio of the export unit value indexes to the import unit value indexes, measured relative to the base year 2015.\n\n### Statistical concept and methodology:\nMethodology: The Net Barter Terms of Trade Index (base year 2015 = 100) is calculated as the ratio of an export price index to an import price index, multiplied by 100. This index measures the rate at which a country can exchange its exports for imports. A value greater than 100 means the terms of trade have improved relative to the base year, allowing the country to obtain more imports for a given level of exports. Conversely, a value below 100 indicates that the terms have worsened.\n\nExport and import price indexes are derived from unit value data or sampled transaction-level data, depending on country-specific reporting practices. The price indexes may be computed using fixed or chain-weighted methods. To ensure temporal comparability, the export and import series are indexed to a common base year (2015 = 100).\n\nThe terms of trade index is sensitive to fluctuations in global commodity prices, exchange rate movements, and changes in trade composition. It is used to assess trade gains or losses, especially for countries reliant on exports of primary commodities. Although it does not capture changes in volumes traded, it provides insight into the relative value received for exports. Policymakers and researchers may use this indicator to monitor competitiveness and evaluate exposure to external shocks.", "origins": [ { "producer": "United Nations Conference on Trade and Development (UNCTAD), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Conference on Trade and Development (UNCTAD). Indicator TT.PRI.MRCH.XD.WD (https://data.worldbank.org/indicator/TT.PRI.MRCH.XD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TT.PRI.MRCH.XD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2015 = 100", "display": { "unit": "2015 = 100" } }, "tx_mnf_tech_zs_un": { "title": "Medium and high-tech exports (% manufactured exports)", "description_from_producer": "Share of medium and high-tech manufactured exports in total manufactured exports.\n\n### Statistical concept and methodology:\nMethodology: The data from UN COMTRADE is downloaded in SITC Revision 3, 3-digit, by reporting country, year, partner code, commodity and flow (export and re-export). SITC medium technology: 266, 267, 512, 513, 533, 553, 554, 562, 571, 572, 573, 574, 575, 579, 581, 582, 583, 591, 593, 597, 598, 653, 671, 672, 678, 711, 712,713, 714, 721, 722, 723, 724, 725, 726, 727, 728, 731, 733, 735, 737, 741, 742, 743, 744, 745, 746, 747, 748, 749, 761, 762, 763, 772, 773, 775, 778, 781, 782, 783, 784, 785, 786, 791, 793, 811, 812, 813, 872, 873, 882, 884, 885; SITC high technology: 525, 541, 542, 716, 718, 751, 752, 759, 764, 771, 774, 776, 792, 871, 874, 881, 891. Net-exports are calculated as exports minus re-exports. Manufactured exports, is the sum of the four categories resource-based exports, low-tech exports, medium tech exports and high-tech exports; and medium-high technology exports, is the sum of medium tech exports and high-tech exports. The world value of manufacturing exports is the sum of all manufacturing net exports. For additional information please see Table B.2.1 in Appendix B of UNIDO (2017): http://stat.unido.org/content/publications/volume-i%252c-competitive-industrial-performance-report-2016\n\n### Development relevance:\nIndustrial development generally entails a structural transition from resource-based and low technology activities to medium and high-tech industry (MHT) activities. A modern, highly complex production structure offers better opportunities for skills development and technological innovation. MHT activities are also the high value addition industries of manufacturing with higher technological intensity and labor productivity. Increasing the share of MHT sectors also reflects the impact of innovation.\n\n### Limitations and exceptions:\nMissing values at country level are imputed based on the methodology from Competitive Industrial Performance Report (UNIDO, 2017).", "origins": [ { "producer": "UN Industrial Development Organization (UNIDO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Competitive Industrial Performance (CIP) database, UN Industrial Development Organization (UNIDO). Indicator TX.MNF.TECH.ZS.UN (https://data.worldbank.org/indicator/TX.MNF.TECH.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.MNF.TECH.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% manufactured exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% manufactured exports" } }, "tx_qty_mrch_xd_wd": { "title": "Export volume index (2015 = 100)", "description_from_producer": "Export volume indexes are derived from UNCTAD's volume index series and are the ratio of the export value indexes to the corresponding unit value indexes. Unit value indexes are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD's estimates using the previous year's trade values at the Standard International Trade Classification three-digit level as weights.\n\n### Statistical concept and methodology:\nMethodology: The Export Volume Index (base year 2015 = 100) measures changes in the physical volume of merchandise exports over time, adjusting for price effects. By The Export Volume Index (base year 2015 = 100) measures changes in the physical volume of merchandise exports over time, adjusting for price effects. By removing the influence of price changes, this index isolates real growth or contraction in exported quantities. A value above 100 indicates that the volume of exports has increased relative to the base year, while a value below 100 signals a reduction.\n\nTo calculate the index, nominal export values are deflated using export price indices or unit value series, depending on the availability of data. These deflated values are then compared to the base year to produce an index that is normalized to 100 in 2015. The index covers all merchandise exports, and values are aggregated at the national level across products and partner economies.\n\nThe Export Volume Index helps distinguish between growth due to increased trade activity and growth driven solely by price inflation. It is particularly relevant for monitoring export performance in real terms, analyzing supply chain trends, and identifying structural shifts in trade composition. This index can inform decisions on industrial policy, export diversification, and investment in production capacity. It may also support economic modeling exercises requiring real trade variables.", "origins": [ { "producer": "United Nations Conference on Trade and Development (UNCTAD), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Conference on Trade and Development (UNCTAD). Indicator TX.QTY.MRCH.XD.WD (https://data.worldbank.org/indicator/TX.QTY.MRCH.XD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.QTY.MRCH.XD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2015 = 100", "display": { "unit": "2015 = 100" } }, "tx_uvi_mrch_xd_wd": { "title": "Export unit value index (2015 = 100)", "description_from_producer": "Export unit value indices come from UNCTAD's trade database. Unit value indices are based on data reported by countries that demonstrate consistency under UNCTAD quality controls, supplemented by UNCTAD\u2019s estimates using the previous year\u2019s trade values at the Standard International Trade Classification three-digit level as weights. To improve data coverage, especially for the latest periods, UNCTAD constructs a set of average prices indexes at the three-digit product classification of the Standard International Trade Classification revision 3 using UNCTAD\u2019s Commodity Price Statistics, interna\u00actional and national sources, and UNCTAD secretariat estimates. This indicator is an index series where 2015=100.\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.", "origins": [ { "producer": "UN Conference on Trade and Development (UNCTAD), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Handbook of Statistics and data files., UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org. Indicator TX.UVI.MRCH.XD.WD (https://data.worldbank.org/indicator/TX.UVI.MRCH.XD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.UVI.MRCH.XD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2015 = 100", "display": { "unit": "2015 = 100" } }, "tx_val_agri_zs_un": { "title": "Agricultural raw materials exports (% of merchandise exports)", "description_from_producer": "Agricultural raw materials comprise section 2 of SITC Rev. 3 (crude materials, inedible, except fuels) excluding divisions 22 (oil-seeds and oleaginous fruits), 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nPrevious editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another.\n\n### Other notes:\nMerchandise export shares may not sum to 100 percent because of unclassified trade.", "origins": [ { "producer": "UN Comtrade, WITS, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB). Indicator TX.VAL.AGRI.ZS.UN (https://data.worldbank.org/indicator/TX.VAL.AGRI.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.AGRI.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of merchandise exports" } }, "tx_val_food_zs_un": { "title": "Food exports (% of merchandise exports)", "description_from_producer": "Food comprises the commodities in SITC (Rev. 3) sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and division 22 (oil seeds, oil nuts, and oil kernels). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nPrevious editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another.\n\n### Other notes:\nMerchandise export shares may not sum to 100 percent because of unclassified trade.", "origins": [ { "producer": "UN Comtrade, WITS, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB). Indicator TX.VAL.FOOD.ZS.UN (https://data.worldbank.org/indicator/TX.VAL.FOOD.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.FOOD.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of merchandise exports" } }, "tx_val_fuel_zs_un": { "title": "Fuel exports (% of merchandise exports)", "description_from_producer": "Fuels comprise the commodities in SITC (Rev. 3) section 3 (mineral fuels, lubricants and related materials). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nPrevious editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another.\n\n### Other notes:\nMerchandise export shares may not sum to 100 percent because of unclassified trade.", "origins": [ { "producer": "UN Comtrade and World Bank (WITS and staff estimates)", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nComtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS), World Bank (WB). Indicator TX.VAL.FUEL.ZS.UN (https://data.worldbank.org/indicator/TX.VAL.FUEL.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.FUEL.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of merchandise exports" } }, "tx_val_ictg_zs_un": { "title": "ICT goods exports (% of total goods exports)", "description_from_producer": "Information and communication technology goods exports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous).\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Information and communication technology goods exports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components, and other information and technology goods (miscellaneous). Software is generally excluded, as there is a preference to record it under services (not an ICT good but an ICT product) to the extent possible. However it is hard to completely exclude embedded software from certain types of ICT goods, such as video game consoles (see for example the discussion on page 30 of the OECD guide cited below). ICT goods exports as a percentage of total goods exports is calculated for each country by dividing the value of its ICT goods exports by the total value of its goods exports. The result is then multiplied by 100 to be expressed as a percentage.\n\nICT goods are defined according to the OECD\u2019s Guide on Measuring the Information Society 2011 for Harmonized System (HS) 2007 and adapted to HS12 by UNCTAD in collaboration with UNSD (United Nations Statistics Division). This new list consists of 93 goods defined at the 6 digit level of the 2012 version of the HS. The technical note is available online at: http://unctad.org/en/PublicationsLibrary/tn_unctad_ict4d02_en.pdf\nData were downloaded from COMTRADE according to the reported classification (HS92, 96, 02, 07, 12) and aggregated into ICT groups by UNCTAD.\n\n### Development relevance:\nThe digital and information revolution has changed the way the world learns, communicates, does business, and treats illnesses. New information and communications technologies (ICT) offer vast opportunities for progress in all walks of life in all countries - opportunities for economic growth, improved health, better service delivery, learning through distance education, and social and cultural advances.\n\nComparable statistics on access, use, quality, and affordability of ICT are needed to formulate growth-enabling policies for the sector and to monitor and evaluate the sector's impact on development. Although basic access data are available for many countries, in most developing countries little is known about who uses ICT; what they are used for (school, work, business, research, government); and how they affect people and businesses. The global Partnership on Measuring ICT for Development is helping to set standards, harmonize information and communications technology statistics, and build statistical capacity in developing countries. For more information see www.itu.int/ITU-D/ict/partnership/.\n\nThe work of the Partnership is directed towards achieving internationally comparable and reliable ICT statistics. In order to achieve this, its members are involved in developing and maintaining a core list of ICT indicators. Other activities include the compilation and dissemination of ICT data, and the provision of technical assistance enabling statistical agencies to collect data that underlie the core list of ICT indicators.\n\n### Limitations and exceptions:\nDetailed trade data are widely available from country trade statistics. These are collected by the UNSD and published in their UN COMTRADE database. The ICT goods trade indicators are usually compiled by interested international and national agencies using COMTRADE data. Concepts are therefore consistent with those applying to the COMTRADE database.\n\nThe main statistical issue associated with this indicator appears to be the different treatment of re-exports and re-imports by countries, depending on whether the Special or General Trade System is used.2 Re-imports are separately reported for some countries and the value of ICT re-imports (which is included in the value of ICT imports for those countries) is generally small.", "origins": [ { "producer": "UNCTADstat - UNCTAD, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNCTADstat database, UN Conference on Trade and Development (UNCTAD), uri: http://unctadstat.unctad.org/ReportFolders/reportFolders.aspx. Indicator TX.VAL.ICTG.ZS.UN (https://data.worldbank.org/indicator/TX.VAL.ICTG.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.ICTG.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total goods exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total goods exports" } }, "tx_val_insf_zs_wt": { "title": "Insurance and financial services (% of commercial service exports)", "description_from_producer": "Financial services covers services related to financial intermediation, financial risk management, liquidity transformation or auxiliary financial activities. It also includes insurance and pension scheme services which are services related to providing life insurance and annuities, non-life insurance, reinsurance, pensions, standardised guarantees and auxiliary services to insurance, pension schemes, and standardised guarantee schemes. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nBalance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator TX.VAL.INSF.ZS.WT (https://data.worldbank.org/indicator/TX.VAL.INSF.ZS.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.INSF.ZS.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of commercial service exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of commercial service exports" } }, "tx_val_manf_zs_un": { "title": "Manufactures exports (% of merchandise exports)", "description_from_producer": "Manufactures comprise commodities in SITC (Rev. 3) sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment), and 8 (miscellaneous manufactured goods), excluding division 68 (non-ferrous metals). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nPrevious editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another.\n\n### Other notes:\nMerchandise export shares may not sum to 100 percent because of unclassified trade.", "origins": [ { "producer": "UN Comtrade, WITS, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB). Indicator TX.VAL.MANF.ZS.UN (https://data.worldbank.org/indicator/TX.VAL.MANF.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MANF.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of merchandise exports" } }, "tx_val_mmtl_zs_un": { "title": "Ores and metals exports (% of merchandise exports)", "description_from_producer": "Ores and metals comprise the commodities in SITC (Rev. 3) sections 27 (crude fertilizer, minerals nes); 28 (metalliferous ores, scrap); and 68 (non-ferrous metals). Exports of services are services provided by residents to non-residents. This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nPrevious editions contained data based on the SITC revision 1. Data for earlier years in previous editions may differ because of the change in methodology. Concordance tables are available to convert data reported in one system to another.\n\n### Other notes:\nMerchandise export shares may not sum to 100 percent because of unclassified trade.", "origins": [ { "producer": "UN Comtrade, WITS, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Comtrade database, United Nations (UN), publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS);\nStaff estimates, World Bank (WB). Indicator TX.VAL.MMTL.ZS.UN (https://data.worldbank.org/indicator/TX.VAL.MMTL.ZS.UN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MMTL.ZS.UN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of merchandise exports" } }, "tx_val_mrch_al_zs": { "title": "Merchandise exports to economies in the Arab World (% of total merchandise exports)", "description_from_producer": "Merchandise exports to economies in the Arab World are the sum of merchandise exports by the reporting economy to economies in the Arab World. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator expresses the percentage of a country\u2019s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.AL.ZS (https://data.worldbank.org/indicator/TX.VAL.MRCH.AL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.AL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise exports" } }, "tx_val_mrch_cd_wt": { "title": "Merchandise exports (current US$)", "description_from_producer": "Merchandise exports includes goods whose economic ownership is changed from a resident to a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: International merchandise trade statistics are compiled in accordance with international standards: International Merchandise Trade statistics \u2013 Concepts and Definitions 2010. Specific information on how countries compile their merchandise trade statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis for international merchandise trade statistics covers a specialized multipurpose domain of official statistics concerned with the provision of data on the movements of goods between countries and areas. Trade statistics are compiled to serve the needs of many users, including Governments; the business community; compilers of other economic statistics, such as balance of payments and national accounts; various regional, supranational and international organizations; researchers; and the public at large. Different users need different data, ranging from data sets by country and commodity at varying levels of detail to aggregated figures.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nExports are recorded as the cost of the goods delivered to the frontier of the exporting country for shipment - the free on board (f.o.b.) value.\n\n\n\n\n\n\n\n\n\nCountries may report trade according to the general or special system of trade. Under the general system exports comprise outward-moving goods that are (a) goods wholly or partly produced in the country; (b) foreign goods, neither transformed nor declared for domestic consumption in the country, that move outward from customs storage; and (c) goods previously included as imports for domestic consumption but subsequently exported without transformation. Under the special system exports comprise categories a and c. In some compilations categories b and c are classified as re-exports. Because of differences in reporting practices, data on exports may not be fully comparable across economies.\n\n\n\n\n\n\n\n\n\nData on exports of goods are derived from the same sources as data on imports. In principle, world exports and imports should be identical. Similarly, exports from an economy should equal the sum of imports by the rest of the world from that economy. But differences in timing and definitions result in discrepancies in reported values at all levels.", "origins": [ { "producer": "World Trade Organization (WTO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Trade Organization (WTO). Indicator TX.VAL.MRCH.CD.WT (https://data.worldbank.org/indicator/TX.VAL.MRCH.CD.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.CD.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "tx_val_mrch_hi_zs": { "title": "Merchandise exports to high-income economies (% of total merchandise exports)", "description_from_producer": "Merchandise exports to high-income economies are the sum of merchandise exports from the reporting economy to high-income economies according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator expresses the percentage of a country\u2019s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies.\n\n### Development relevance:\nLow- and middle-income economies are an increasingly important part of the global trading system. Trade between high-income economies and low- and middle-income economies has grown faster than trade between high-income economies. This increased trade benefits both producers and consumers in developing and high-income economies.\n\nAt the regional level most exports from low- and middle-income economies are to high-income economies, but the share of intraregional trade is increasing. Geographic patterns of trade vary widely by country and commodity. Larger shares of exports from oil- and resource-rich economies are to high-income economies.\n\n### Limitations and exceptions:\nData on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.HI.ZS (https://data.worldbank.org/indicator/TX.VAL.MRCH.HI.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.HI.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise exports" } }, "tx_val_mrch_or_zs": { "title": "Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)", "description_from_producer": "Merchandise exports to low- and middle-income economies outside region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in other World Bank regions according to the World Bank classification of economies. Data are expressed as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator expresses the percentage of a country\u2019s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies.\n\n### Development relevance:\nAlthough global integration has increased, low- and middle-income economies still face trade barriers when accessing other markets.\n\n### Limitations and exceptions:\nData on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.OR.ZS (https://data.worldbank.org/indicator/TX.VAL.MRCH.OR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.OR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise exports" } }, "tx_val_mrch_r1_zs": { "title": "Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)", "description_from_producer": "Merchandise exports to low- and middle-income economies in East Asia and Pacific are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the East Asia and Pacific region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator expresses the percentage of a country\u2019s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.R1.ZS (https://data.worldbank.org/indicator/TX.VAL.MRCH.R1.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.R1.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise exports" } }, "tx_val_mrch_r2_zs": { "title": "Merchandise exports to low- and middle-income economies in Europe & Central Asia (% of total merchandise exports)", "description_from_producer": "Merchandise exports to low- and middle-income economies in Europe and Central Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Europe and Central Asia region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator expresses the percentage of a country\u2019s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.R2.ZS (https://data.worldbank.org/indicator/TX.VAL.MRCH.R2.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.R2.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise exports" } }, "tx_val_mrch_r3_zs": { "title": "Merchandise exports to low- and middle-income economies in Latin America & the Caribbean (% of total merchandise exports)", "description_from_producer": "Merchandise exports to low- and middle-income economies in Latin America and the Caribbean are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Latin America and the Caribbean region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator expresses the percentage of a country\u2019s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.R3.ZS (https://data.worldbank.org/indicator/TX.VAL.MRCH.R3.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.R3.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise exports" } }, "tx_val_mrch_r4_zs": { "title": "Merchandise exports to low- and middle-income economies in Middle East & North Africa (% of total merchandise exports)", "description_from_producer": "Merchandise exports to low- and middle-income economies in Middle East and North Africa are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Middle East and North Africa region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator expresses the percentage of a country\u2019s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.R4.ZS (https://data.worldbank.org/indicator/TX.VAL.MRCH.R4.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.R4.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise exports" } }, "tx_val_mrch_r5_zs": { "title": "Merchandise exports to low- and middle-income economies in South Asia (% of total merchandise exports)", "description_from_producer": "Merchandise exports to low- and middle-income economies in South Asia are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the South Asia region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator expresses the percentage of a country\u2019s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.R5.ZS (https://data.worldbank.org/indicator/TX.VAL.MRCH.R5.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.R5.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise exports" } }, "tx_val_mrch_r6_zs": { "title": "Merchandise exports to low- and middle-income economies in Sub-Saharan Africa (% of total merchandise exports)", "description_from_producer": "Merchandise exports to low- and middle-income economies in Sub-Saharan Africa are the sum of merchandise exports from the reporting economy to low- and middle-income economies in the Sub-Saharan Africa region according to World Bank classification of economies. Data are as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator expresses the percentage of a country\u2019s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.R6.ZS (https://data.worldbank.org/indicator/TX.VAL.MRCH.R6.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.R6.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise exports" } }, "tx_val_mrch_rs_zs": { "title": "Merchandise exports by the reporting economy, residual (% of total merchandise exports)", "description_from_producer": "Merchandise exports by the reporting economy residuals are the total merchandise exports by the reporting economy to the rest of the world as reported in the IMF's Direction of trade database, less the sum of exports by the reporting economy to high-, low-, and middle-income economies according to the World Bank classification of economies. Includes trade with unspecified partners or with economies not covered by World Bank classification. Data are as a percentage of total merchandise exports by the economy.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator presents the total value of all merchandise exports made by the reporting country over a given period, typically a calendar year. Exports are measured in nominal terms using current U.S. dollars and represent the total value of goods crossing the economic border of the country for sale, exchange, or further processing abroad. Data are reported by national customs authorities and compiled by statistical offices.\n\nExport values are recorded on a free-on-board (FOB) basis, which includes the cost of goods up to the point of shipment, excluding transportation and insurance costs beyond the exporting country. All merchandise goods are included, regardless of their final use in the destination country. Re-exports may be included or excluded depending on national reporting practices; when excluded, the values represent domestic exports only. Goods exported for processing, assembly, or resale are included if ownership is transferred to a foreign entity.\n\nThis indicator provides a comprehensive view of the country's trade performance, export capacity, and integration into the global market. Changes in total exports can reflect economic cycles, shifts in production and competitiveness, changes in external demand, or exchange rate dynamics. Policymakers, economists, and trade analysts often rely on this measure to assess external sector performance, inform macroeconomic modeling, or design export promotion strategies.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.RS.ZS (https://data.worldbank.org/indicator/TX.VAL.MRCH.RS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.RS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise exports" } }, "tx_val_mrch_wl_cd": { "title": "Merchandise exports by the reporting economy (current US$)", "description_from_producer": "Merchandise exports by the reporting economy are the total merchandise exports by the reporting economy to the rest of the world, as reported in the IMF's Direction of trade database. Data are in current US$.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: \"This indicator presents the total value of all merchandise exports made by the reporting country over a given period, typically a calendar year. Exports are This This indicator presents the total value of all merchandise exports made by the reporting country over a given period, typically a calendar year. Exports are measured in nominal terms using current U.S. dollars and represent the total value of goods crossing the economic border of the country for sale, exchange, or further processing abroad. Data are reported by national customs authorities and compiled by statistical offices.\n\nExport values are recorded on a free-on-board (FOB) basis, which includes the cost of goods up to the point of shipment, excluding transportation and insurance costs beyond the exporting country. All merchandise goods are included, regardless of their final use in the destination country. Re-exports may be included or excluded depending on national reporting practices; when excluded, the values represent domestic exports only. Goods exported for processing, assembly, or resale are included if ownership is transferred to a foreign entity.\n\nThis indicator provides a comprehensive view of the country\u2019s trade performance, export capacity, and integration into the global market. Changes in total exports can reflect economic cycles, shifts in production and competitiveness, changes in external demand, or exchange rate dynamics. Policymakers, economists, and trade analysts often rely on this measure to assess external sector performance, inform macroeconomic modeling, or design export promotion strategies.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.WL.CD (https://data.worldbank.org/indicator/TX.VAL.MRCH.WL.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.WL.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "tx_val_mrch_wr_zs": { "title": "Merchandise exports to low- and middle-income economies within region (% of total merchandise exports)", "description_from_producer": "Merchandise exports to low- and middle-income economies within region are the sum of merchandise exports from the reporting economy to other low- and middle-income economies in the same World Bank region as a percentage of total merchandise exports by the economy. Data are computed only if at least half of the economies in the partner country group had non-missing data. No figures are shown for high-income economies, because they are a separate category in the World Bank classification of economies.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: This indicator expresses the percentage of a country\u2019s total merchandise exports that are destined for a specific group of economies, such as high-income countries, the Arab World, or various regional and development classifications. It is calculated as the ratio between the value of goods exported to the specified group and the total value of all merchandise exports of the reporting economy in the same period. All export values are expressed in current U.S. dollars and originate from customs records collected by national authorities.\n\nThe destination of merchandise exports is identified through shipping documentation and export declarations, typically using the last known destination country at the time of customs clearance. Classification of partner economies is based on lists reflecting regional membership, development level, or income groupings as used at the time of reporting. For example, high-income economies are defined by income thresholds, while other groups like the Arab World are identified by political-geographic criteria.\n\nExport data are usually reported on a free-on-board (FOB) basis, meaning the value reflects the cost of goods at the point of shipment, excluding insurance and freight beyond the port of departure. The indicator provides a measure of export market concentration and can be used to analyze changes in trade orientation, dependency on particular economic groups, or regional integration strategies. Policymakers, analysts, and trade negotiators may use this metric to monitor shifts in export destinations due to new trade agreements, geopolitical developments, supply chain changes, or shifts in global demand. Fluctuations may also reflect external shocks such as sanctions or disruptions in partner economies.\n\n### Development relevance:\nThe relative importance of intraregional trade is higher for both landlocked countries and small countries with close trade links to the largest regional economy. For most low- and middle-income economies - especially smaller ones - there is a \"geographic bias\" favoring intraregional trade. Despite the broad trend toward globalization and the reduction of trade barriers, the relative share of intraregional trade increased for most economies between 1999 and 2010. This is due partly to trade-related advantages, such as proximity, lower transport costs, increased knowledge from repeated interaction, and cultural and historical affinity. The direction of trade is also influenced by preferential trade agreements that a country has made with other economies. Though formal agreements on trade liberalization do not automatically increase trade, they nevertheless affect the direction of trade between the participating economies.\n\n### Limitations and exceptions:\nData on exports and imports are from the International Monetary Fund's (IMF) Direction of Trade database and should be broadly consistent with data from other sources, such as the United Nations Statistics Division's Commodity Trade (Comtrade) database. All high-income economies and major low- and middle-income economies report trade data to the IMF on a timely basis, covering about 85 percent of trade for recent years. Trade data for less timely reporters and for countries that do not report are estimated using reports of trading partner countries. Therefore, data on trade between developing and high-income economies should be generally complete. But trade flows between many low- and middle-income economies - particularly those in Sub-Saharan Africa - are not well recorded, and the value of trade among low- and middle-income economies may be understated.", "origins": [ { "producer": "World Bank staff estimates and IMF Direction of Trade database", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Staff estimates, World Bank (WB);\nDirection of Trade database, International Monetary Fund (IMF). Indicator TX.VAL.MRCH.WR.ZS (https://data.worldbank.org/indicator/TX.VAL.MRCH.WR.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.WR.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total merchandise exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total merchandise exports" } }, "tx_val_mrch_xd_wd": { "title": "Export value index (2015 = 100)", "description_from_producer": "Export values are the current value of exports (f.o.b.) converted to U.S. dollars and expressed as a percentage of the average for the base period (2015). UNCTAD's export value indexes are reported for most economies.\n\n### Statistical concept and methodology:\nMethodology: The value indices show the current value of exports (f.o.b.) for an economy, after conversion to United States dollars and with the reference year =100. Thus it is simply derived from monetary values from the total trade series or from the trade matrix and exchange rates.", "origins": [ { "producer": "United Nations Conference on Trade and Development (UNCTAD), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UN Conference on Trade and Development (UNCTAD). Indicator TX.VAL.MRCH.XD.WD (https://data.worldbank.org/indicator/TX.VAL.MRCH.XD.WD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.MRCH.XD.WD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "2015 = 100", "display": { "unit": "2015 = 100" } }, "tx_val_othr_zs_wt": { "title": "Computer, communications and other services (% of commercial service exports)", "description_from_producer": "Computer, communications and other services include such activities as international telecommunications, and postal and courier services; computer data; news-related service transactions between residents and nonresidents; construction services; royalties and license fees; miscellaneous business, professional, and technical services; and personal, cultural, and recreational services. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nBalance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator TX.VAL.OTHR.ZS.WT (https://data.worldbank.org/indicator/TX.VAL.OTHR.ZS.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.OTHR.ZS.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of commercial service exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of commercial service exports" } }, "tx_val_serv_cd_wt": { "title": "Commercial service exports (current US$)", "description_from_producer": "Commercial service exports are total service exports minus exports of government services not included elsewhere. Exports of services are services provided by residents to non-residents. This indicator is expressed in current prices, meaning no adjustment has been made to account for price changes over time. This indicator is expressed in United States dollars.\n\n### Aggregation method:\nGap-filled total\n\n### Statistical concept and methodology:\nMethodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nBalance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator TX.VAL.SERV.CD.WT (https://data.worldbank.org/indicator/TX.VAL.SERV.CD.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.SERV.CD.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "tx_val_tech_cd": { "title": "High-technology exports (current US$)", "description_from_producer": "High-technology exports are products with high R&D intensity, such as aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Data for high-technology exports are sourced from the United Nations Comtrade database and accessed through the World Integrated Trade Solution (WITS) platform. Export values are calculated as gross exports minus re-exports, using country-reported trade statistics. The methodology aggregates the value of exports for all products classified as high-technology according to SITC Rev.4 codes, as defined by Eurostat and the OECD. Periodic updates to product codes and conversion tables (e.g., from HS to SITC) are incorporated to maintain consistency and address data gaps. The indicator is published annually, and methodological adjustments\u2014such as the inclusion of additional product codes to correct for conversion issues\u2014are documented in the metadata to ensure transparency and comparability over time.\nStatistical concept(s): High technology products are defined according to SITC Rev.4 as the sum of the following products: Aerospace, Computers-office machines, Electronics-telecommunications, Pharmacy, Scientific instruments, Electrical machinery, Chemistry, Non-electrical machinery, Armament. The following product codes are used: Aerospace: (714 \u2013 71489 -71499)+7921+7922+7924+7925+79291+79293+87411; Computers-office machines: 75194+75195+752+75997; Electronics-communication: 76331+7638+(764-76493-76499)+7722+77261+77318+77625+77627+7763+7764+7768+89844+89846; Pharmacy: 5413+5415+5416+5421+5422; Scientific instruments: 774+871+87211+(874-87411-8742)+88111+88121+88411+88419+(8996-89965-89969); Electrical machinery: (7786-77861-777866-77869)+7787+77884; Chemistry: 52222+52223+52229+52269+525+531+57433+591; Non-electrical machinery: 71489+71499+7187+72847+7311+73131+73135+73142+73144+73151+73153+73161+73163+73165+73312+73314+73316+7359+73733+73735; Armament: 891\nThe list can also be accessed on the Eurostat website. This list, based on the OECD definition, contains technical products of which the manufacturing involved a high intensity of R&D. The original high-tech products classification is based on SITC Rev. 3 and is taken from Table 4 of Annex 2 of the 1997 working paper of Thomas Hatzichronouglou, OECD. In September 2019 the definition in the World Development Indicators database was updated to SITC Rev.4 from SITC Rev. 3. The data are in current U.S. dollars and are sourced from the UN's Comtrade database.\n\nNote: The definition of high technology exports in WDI was modified as of October 2024. Specifically the list of SITC Rev.4 high technology product codes now includes product code 776 in its entirety and that all data in its sub-categories is incorporated. This means that products codes 776.11, 776.12, 776.21, 776.23, 776.29 have been added to the high-tech list of products, which does not comply with OECD\u2019s 2008 definition of high-tech.\n\nThis change was implemented to address the problem of missing data which occurs during the data conversion process from HS2022 to SITC4. This happens if a country submits export data in the HS2022 coding system and is no longer in HS2017. Specifically, the HS2022-to-SITC4 conversion table (downloaded from COMTRADE) does not break down product code 776.4+ into sub-categories, and the export data of these sub-categories has been subsumed into 776. Since there is no break down for 776.4+, extracting data from the SITC4 dataset in the COMTRADE database will yield zero value for SITC4 codes 776.4, 776.42, 776.44, 776.46, and 776.49 as defined in the OECD high-tech definition. Hence, when calculating high-tech export values using the forward method of summing up data from these five SITC4 codes will result in missing data caused by the conversion process. This can result in a significant year-on-year decline in high-tech export values for affected countries.\n\n### Development relevance:\nHigh-technology exports represent the value of products with high research and development (R&D) intensity, such as aerospace, computers, pharmaceuticals, scientific instruments, and advanced machinery, exported by a country in a given year. High-tech exports are defined according to a standard classification (based on SITC Rev.4, using a product-level approach originally developed by OECD in collaboration with Eurostat), which aggregates product codes associated with high R&D intensity.\n\nFrom a development perspective, tracking high-technology exports reveals structural shifts in an economy: a rising volume of such exports may signal successful technology transfer, increasing domestic capacity for innovation, and greater economic sophistication. It provides empirical evidence of movement toward higher value-added production, which is often associated with stronger productivity growth, better jobs, improved trade balances, and long-term competitiveness.\n\nMoreover, by benchmarking across countries, the indicator helps policymakers and researchers assess how well an economy is diversifying away from resource- or low-technology-based exports and embedding itself in segments of global trade characterized by rapid innovation and technological change. Tracking high-technology exports also helps policymakers identify strengths and gaps in national innovation systems, benchmark progress against peers, and design targeted interventions to foster knowledge-intensive industries and diversify export portfolios.\n\n### Limitations and exceptions:\nThe indicator is based on data reported by countries to COMTRADE. The export values presented in the World Development Indicators represent Gross Exports less Re-Exports. The values may be impacted in cases of reporting errors or missing data, for example if countries do not report Re-Exports for one or more periods.", "origins": [ { "producer": "United Nations Comtrade database and World Integrated Trade Solution (WITS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Comtrade database, United Nations (UN), uri: comtrade.un.org, publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS), World Bank (WB), uri: https://wits.worldbank.org/. Indicator TX.VAL.TECH.CD (https://data.worldbank.org/indicator/TX.VAL.TECH.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.TECH.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "tx_val_tech_mf_zs": { "title": "High-technology exports (% of manufactured exports)", "description_from_producer": "High-technology exports are products with high R&D intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: Data for high-technology exports are sourced from the United Nations Comtrade database and accessed through the World Integrated Trade Solution (WITS) platform. Export values are calculated as gross exports minus re-exports, using country-reported trade statistics. The methodology aggregates the value of exports for all products classified as high-technology according to SITC Rev.4 codes, as defined by Eurostat and the OECD. Periodic updates to product codes and conversion tables (e.g., from HS to SITC) are incorporated to maintain consistency and address data gaps. The indicator is published annually, and methodological adjustments\u2014such as the inclusion of additional product codes to correct for conversion issues\u2014are documented in the metadata to ensure transparency and comparability over time.\nStatistical concept(s): High technology products are defined according to SITC Rev.4 as the sum of the following products: Aerospace, Computers-office machines, Electronics-telecommunications, Pharmacy, Scientific instruments, Electrical machinery, Chemistry, Non-electrical machinery, Armament. The following product codes are used: Aerospace: (714 \u2013 71489 -71499)+7921+7922+7924+7925+79291+79293+87411; Computers-office machines: 75194+75195+752+75997; Electronics-communication: 76331+7638+(764-76493-76499)+7722+77261+77318+77625+77627+7763+7764+7768+89844+89846; Pharmacy: 5413+5415+5416+5421+5422; Scientific instruments: 774+871+87211+(874-87411-8742)+88111+88121+88411+88419+(8996-89965-89969); Electrical machinery: (7786-77861-777866-77869)+7787+77884; Chemistry: 52222+52223+52229+52269+525+531+57433+591; Non-electrical machinery: 71489+71499+7187+72847+7311+73131+73135+73142+73144+73151+73153+73161+73163+73165+73312+73314+73316+7359+73733+73735; Armament: 891\nThe list can also be accessed on the Eurostat website. This list, based on the OECD definition, contains technical products of which the manufacturing involved a high intensity of R&D. The original high-tech products classification is based on SITC Rev. 3 and is taken from Table 4 of Annex 2 of the 1997 working paper of Thomas Hatzichronouglou, OECD. In September 2019 the definition in the World Development Indicators database was updated to SITC Rev.4 from SITC Rev. 3. The data are in current U.S. dollars and are sourced from the UN's Comtrade database.\n\nNote: The definition of high technology exports in WDI was modified as of October 2024. Specifically the list of SITC Rev.4 high technology product codes now includes product code 776 in its entirety and that all data in its sub-categories is incorporated. This means that products codes 776.11, 776.12, 776.21, 776.23, 776.29 have been added to the high-tech list of products, which does not comply with OECD\u2019s 2008 definition of high-tech.\n\nThis change was implemented to address the problem of missing data which occurs during the data conversion process from HS2022 to SITC4. This happens if a country submits export data in the HS2022 coding system and is no longer in HS2017. Specifically, the HS2022-to-SITC4 conversion table (downloaded from COMTRADE) does not break down product code 776.4+ into sub-categories, and the export data of these sub-categories has been subsumed into 776. Since there is no break down for 776.4+, extracting data from the SITC4 dataset in the COMTRADE database will yield zero value for SITC4 codes 776.4, 776.42, 776.44, 776.46, and 776.49 as defined in the OECD high-tech definition. Hence, when calculating high-tech export values using the forward method of summing up data from these five SITC4 codes will result in missing data caused by the conversion process. This can result in a significant year-on-year decline in high-tech export values for affected countries.\n\n### Development relevance:\nHigh-technology exports represent the value of products with high research and development (R&D) intensity, such as aerospace, computers, pharmaceuticals, scientific instruments, and advanced machinery, exported by a country in a given year. High-tech exports are defined according to a standard classification (based on SITC Rev.4, using a product-level approach originally developed by OECD in collaboration with Eurostat), which aggregates product codes associated with high R&D intensity.\n\nFrom a development perspective, tracking high-technology exports reveals structural shifts in an economy: a rising volume of such exports may signal successful technology transfer, increasing domestic capacity for innovation, and greater economic sophistication. It provides empirical evidence of movement toward higher value-added production, which is often associated with stronger productivity growth, better jobs, improved trade balances, and long-term competitiveness.\n\nMoreover, by benchmarking across countries, the indicator helps policymakers and researchers assess how well an economy is diversifying away from resource- or low-technology-based exports and embedding itself in segments of global trade characterized by rapid innovation and technological change. Tracking high-technology exports also helps policymakers identify strengths and gaps in national innovation systems, benchmark progress against peers, and design targeted interventions to foster knowledge-intensive industries and diversify export portfolios.\n\n### Limitations and exceptions:\nThe indicator is based on data reported by countries to COMTRADE. The export values presented in the World Development Indicators represent Gross Exports less Re-Exports. The values may be impacted in cases of reporting errors or missing data, for example if countries do not report Re-Exports for one or more periods.", "origins": [ { "producer": "United Nations Comtrade database and World Integrated Trade Solution (WITS), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Comtrade database, United Nations (UN), uri: comtrade.un.org, publisher: UN Statistics Division;\nWorld Integrated Trade Solution system (WITS), World Bank (WB), uri: https://wits.worldbank.org/. Indicator TX.VAL.TECH.MF.ZS (https://data.worldbank.org/indicator/TX.VAL.TECH.MF.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.TECH.MF.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of manufactured exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of manufactured exports" } }, "tx_val_tran_zs_wt": { "title": "Transport services (% of commercial service exports)", "description_from_producer": "Transport is the process of carriage of people and objects from one location to another as well as related supporting and auxiliary services. Also included are postal and courier services. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nBalance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator TX.VAL.TRAN.ZS.WT (https://data.worldbank.org/indicator/TX.VAL.TRAN.ZS.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.TRAN.ZS.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of commercial service exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of commercial service exports" } }, "tx_val_trvl_zs_wt": { "title": "Travel services (% of commercial service exports)", "description_from_producer": "Travel services cover goods and services for own use or to give away acquired from an economy by nonresidents during visits to that economy, or acquired from other economies by residents during visits to these other economies. This indicator is expressed as a percentage of service exports which are commercial services provided by residents to non-residents.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: International trade in services statistics are compiled in accordance with international standards: Manual on Statistics of International Trade in Services 2010. Specific information on how countries compile their trade in service statistics can be found on the IMF website: https://dsbb.imf.org/\nStatistical concept(s): The conceptual basis of international trade in services covers trade in services in the conventional sense of transactions (exports and imports) between residents and non-residents. In addition, it covers services delivered through enterprises that are locally established but foreign-controlled and cases where individuals are temporarily present abroad for the purpose of supplying a service.\n\n### Development relevance:\nThis indicator is related to the trade statistics, which are essential for gauging a country's economic performance, particularly through the lens of its trade balance, which is the net of exports against imports. These statistics inform government trade policy, trade agreement negotiations, and decisions on tariffs and other trade barriers. For businesses, this information is crucial for strategic decisions about export and import locations, market entry, and product pricing. By enabling comparisons between nations, trade data sheds light on competitive strengths and the movement of goods and services internationally. It's also key for monitoring trade trends, including the rise of new markets or shifts in commodity demand, and for identifying both opportunities for growth and potential economic risks. Trade volumes and values are important economic indicators, offering insights into the economic health of a nation and affecting investment decisions and forecasts. Overall, trade statistics play a central role in understanding the complexities of global trade and in guiding both macroeconomic policy and microeconomic business decisions.\n\n### Limitations and exceptions:\nBalance of payments statistics, the main source of information on international trade in services, have many weaknesses. Disaggregation of important components may be limited and varies considerably across countries. There are inconsistencies in the methods used to report items. And the recording of major flows as net items is common (for example, insurance transactions are often recorded as premiums less claims). These factors contribute to a downward bias in the value of the service trade reported in the balance of payments.\n\n\n\n\n\n\n\n\n\nEfforts are being made to improve the coverage, quality, and consistency of these data. Eurostat and the Organisation for Economic Co-operation and Development, for example, are working together to improve the collection of statistics on trade in services in member countries.\n\n\n\n\n\n\n\n\n\nStill, difficulties in capturing all the dimensions of international trade in services mean that the record is likely to remain incomplete. Cross-border intrafirm service transactions, which are usually not captured in the balance of payments, have increased in recent years. An example is transnational corporations' use of mainframe computers around the clock for data processing, exploiting time zone differences between their home country and the host countries of their affiliates. Another important dimension of service trade not captured by conventional balance of payments statistics is establishment trade - sales in the host country by foreign affiliates. By contrast, cross-border intrafirm transactions in merchandise may be reported as exports or imports in the balance of payments.", "origins": [ { "producer": "International Monetary Fund (IMF), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Balance of Payments Statistics Yearbook and data files, International Monetary Fund (IMF). Indicator TX.VAL.TRVL.ZS.WT (https://data.worldbank.org/indicator/TX.VAL.TRVL.ZS.WT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/TX.VAL.TRVL.ZS.WT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of commercial service exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of commercial service exports" } }, "vc_btl_deth": { "title": "Battle-related deaths (number of people)", "description_from_producer": "Battle-related deaths are deaths in battle-related conflicts between warring parties in the conflict dyad (two conflict units that are parties to a conflict). Battle-related deaths refer to those deaths caused by the warring parties that can be directly related to combat. This includes battlefield fighting, guerrilla activities (e.g. hit and-run attacks/ambushes) and all kinds of bombardments of military bases, cities and villages etc. The target for the attacks is either the military forces or representatives for the parties, though there is often substantial collateral damage in the form of civilians being killed in the crossfire, indiscriminate bombings, etc. All fatalities, military as well as civilian, incurred in such situations are counted as battle-related deaths.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The dataset is constructed by automatically filtering and aggregating the UCDP Georeferenced Event Dataset from the incident level to the conflict/dyad-year level, and then joining this with additional conflict-year data from related UCDP datasets. Source material is gathered in a two-pass system: first, global newswire reporting is reviewed, followed by targeted searches in local and specialized sources such as local media, NGO and IGO reports, field reports, and social media. This approach ensures comprehensive coverage and cross-verification of events. Detailed procedures, including search strategies, are further described in the UCDP GED Codebook.\n\n\nStatistical concept(s): The UCDP provides three estimates for battle-related deaths (best, low, and high) to account for the uncertainty inherent in conflict reporting. The \"best estimate\" (which is the value available in the World Development Indicators database) aggregates the most reliable numbers for all incidents each year, favoring the lower figure when sources are equally credible. The \"low estimate\" sums the lowest plausible figures, while the \"high estimate\" aggregates the highest plausible numbers, including incidents with uncertain party involvement. All estimates are based on publicly accessible sources and are subject to revision as new information emerges.\n\n### Development relevance:\nMonitoring battle-related deaths is critical for understanding the human cost of conflict and its implications for development. High levels of conflict-related mortality often coincide with displacement, destruction of infrastructure, and disruption of economic activity, which can reverse progress on poverty reduction and human development. Reliable data on conflict intensity inform policy responses aimed at peacebuilding, humanitarian assistance, and post-conflict recovery. They also support global efforts to track progress toward peace and security goals, by providing evidence for interventions that reduce violence and promote stability.\n\n### Limitations and exceptions:\nWhile the best estimate for battle-related deaths is considered a conservative minimum, the high estimate may still undercount actual fatalities due to unreported events, and users are cautioned that the apparent precision of the numbers does not eliminate underlying uncertainty.", "origins": [ { "producer": "Uppsala Conflict Data Program (UCDP), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UCDP Battle-related Deaths Dataset , Uppsala Conflict Data Program (UCDP), uri: https://ucdp.uu.se/downloads/, publisher: Uppsala University. Indicator VC.BTL.DETH (https://data.worldbank.org/indicator/VC.BTL.DETH). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/VC.BTL.DETH", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "number of people", "display": { "unit": "number of people" } }, "vc_idp_nwcv": { "title": "Internally displaced persons, new displacement associated with conflict and violence (number of cases)", "description_from_producer": "Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. \"New Displacement\" refers to the number of new cases or incidents of displacement recorded over the specified year, rather than the number of people displaced. This is done because people may have been displaced more than once.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Internally displaced persons are \"persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border.\" Internally displaced people are often confused with refugees. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees. Refugees are people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection. For more information on methodology, please refer to the information published by IDMC: http://www.internal-displacement.org/database/\n\n### Development relevance:\nAlthough all persons affected by conflict and/or human rights violations suffer, displacement from one's place of residence may make the internally displaced particularly vulnerable. Following are some of the factors that are likely to increase the need for protection:\n\n1) Internally displaced persons may be in transit from one place to another, may be in hiding, may be forced toward unhealthy or inhospitable environments, or face other circumstances that make them especially vulnerable.\n\n2) The social organization of displaced communities may have been destroyed or damaged by the act of physical displacement; family groups may be separated or disrupted; women may be forced to assume non-traditional roles or face particular vulnerabilities. Internally displaced populations, and especially groups like children, the elderly, or pregnant women, may experience profound psychosocial distress related to displacement.\n\n3) Removal from sources of income and livelihood may add to physical and psychosocial vulnerability for displaced people.\n\n4) Schooling for children and adolescents may be disrupted.\n\n5) Internal displacement to areas where local inhabitants are of different groups or inhospitable may increase risk to internally displaced communities; internally displaced persons may face language barriers during displacement.\n\n6) The condition of internal displacement may raise the suspicions of or lead to abuse by armed combatants, or other parties to conflict.\n\n7) Internally displaced persons may lack identity documents essential to receiving benefits or legal recognition; in some cases, fearing persecution, displaced persons have sometimes got rid of such documents.\n\n8) According to the Internal Displacement Monitoring Centre (IDMC) tens of millions people around the world are displaced every year within their countries by conflict, human rights violations, natural disasters and climate change. Unlike refugees who cross national borders and benefit from an established system of international protection and assistance, those forcibly uprooted within their own countries, by armed conflict, large-scale development projects, systematic violations of human rights, or natural disasters, lack predictable structures of support. Internal displacement has become one of the more pressing humanitarian, human rights and security problems confronting affected countries and the international community at large.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights.\n\n### Limitations and exceptions:\nPlease note that most of the figures are estimates. The definition highlights two issues:\n\n1) The coercive or otherwise involuntary character of movement. The definition mentions some of the most common causes of involuntary movements, such as armed conflict, violence, human rights violations and disasters. These causes have in common that they give no choice to people but to leave their homes and deprive them of the most essential protection mechanisms, such as community networks, access to services, livelihoods. Displacement severely affects the physical, socio-economic and legal safety of people and should be systematically regarded as an indicator of potential vulnerability.\n\n2) The fact that such movement takes place within national borders. Unlike refugees, who have been deprived of the protection of their state of origin, IDPs remain legally under the protection of national authorities of their country of habitual residence. IDPs should therefore enjoy the same rights as the rest of the population. The Guiding Principles on Internal Displacement remind national authorities and other relevant actors of their responsibility to ensure that IDPs' rights are respected and fulfilled, despite the vulnerability generated by their displacement.", "origins": [ { "producer": "Internal Displacement Monitoring Centre (IDMC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Internal Displacement Monitoring Centre (IDMC), uri: http://www.internal-displacement.org/. Indicator VC.IDP.NWCV (https://data.worldbank.org/indicator/VC.IDP.NWCV). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/VC.IDP.NWCV", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "number of cases", "display": { "unit": "number of cases" } }, "vc_idp_nwds": { "title": "Internally displaced persons, new displacement associated with disasters (number of cases)", "description_from_producer": "Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. \"New Displacement\" refers to the number of new cases or incidents of displacement recorded over the specified year, rather than the number of people displaced. This is done because people may have been displaced more than once.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Internally displaced persons are \"persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border.\" Internally displaced people are often confused with refugees. Unlike refugees, internally displaced people remain under the protection of their own government, even if their reason for fleeing was similar to that of refugees. Refugees are people who have crossed an international border to find sanctuary and have been granted refugee or refugee-like status or temporary protection.\n\n### Development relevance:\nAlthough all persons affected by conflict and/or human rights violations suffer, displacement from one's place of residence may make the internally displaced particularly vulnerable. Following are some of the factors that are likely to increase the need for protection:\n\n1) Internally displaced persons may be in transit from one place to another, may be in hiding, may be forced toward unhealthy or inhospitable environments, or face other circumstances that make them especially vulnerable.\n\n2) The social organization of displaced communities may have been destroyed or damaged by the act of physical displacement; family groups may be separated or disrupted; women may be forced to assume non-traditional roles or face particular vulnerabilities. Internally displaced populations, and especially groups like children, the elderly, or pregnant women, may experience profound psychosocial distress related to displacement.\n\n3) Removal from sources of income and livelihood may add to physical and psychosocial vulnerability for displaced people.\n\n4) Schooling for children and adolescents may be disrupted.\n\n5) Internal displacement to areas where local inhabitants are of different groups or inhospitable may increase risk to internally displaced communities; internally displaced persons may face language barriers during displacement.\n\n6) The condition of internal displacement may raise the suspicions of or lead to abuse by armed combatants, or other parties to conflict.\n\n7) Internally displaced persons may lack identity documents essential to receiving benefits or legal recognition; in some cases, fearing persecution, displaced persons have sometimes got rid of such documents.\n\n8) According to the Internal Displacement Monitoring Centre (IDMC) tens of millions people around the world are displaced every year within their countries by conflict, human rights violations, natural disasters and climate change. Unlike refugees who cross national borders and benefit from an established system of international protection and assistance, those forcibly uprooted within their own countries, by armed conflict, large-scale development projects, systematic violations of human rights, or natural disasters, lack predictable structures of support. Internal displacement has become one of the more pressing humanitarian, human rights and security problems confronting affected countries and the international community at large.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights.\n\n### Limitations and exceptions:\nPlease note that most of the figures are estimates. The definition highlights two issues:\n\n1) The coercive or otherwise involuntary character of movement. The definition mentions some of the most common causes of involuntary movements, such as armed conflict, violence, human rights violations and disasters. These causes have in common that they give no choice to people but to leave their homes and deprive them of the most essential protection mechanisms, such as community networks, access to services, livelihoods. Displacement severely affects the physical, socio-economic and legal safety of people and should be systematically regarded as an indicator of potential vulnerability.\n\n2) The fact that such movement takes place within national borders. Unlike refugees, who have been deprived of the protection of their state of origin, IDPs remain legally under the protection of national authorities of their country of habitual residence. IDPs should therefore enjoy the same rights as the rest of the population. The Guiding Principles on Internal Displacement remind national authorities and other relevant actors of their responsibility to ensure that IDPs' rights are respected and fulfilled, despite the vulnerability generated by their displacement.", "origins": [ { "producer": "Internal Displacement Monitoring Centre (IDMC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Internal Displacement Monitoring Centre (IDMC), uri: http://www.internal-displacement.org/. Indicator VC.IDP.NWDS (https://data.worldbank.org/indicator/VC.IDP.NWDS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/VC.IDP.NWDS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "number of cases", "display": { "unit": "number of cases" } }, "vc_ihr_psrc_fe_p5": { "title": "Intentional homicides, female (per 100,000 female)", "description_from_producer": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury.\n\n### Aggregation method:\nAggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf\n\n### Statistical concept and methodology:\nMethodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual.\n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation.\n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States.\n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs.\nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS.\n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury.\n\nThe statistical definition contains three elements that characterize the killing of a person as \u201cintentional homicide\u201d:\n1. The killing of a person by another person (objective element)\n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element)\n3. The unlawfulness of the killing (legal element)\n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide.\n\n### Development relevance:\nIn some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict.\n\n### Limitations and exceptions:\nStatistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data.", "origins": [ { "producer": "UN Office on Drugs and Crime (UNODC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNODC Research - Data Portal \u2013 Intentional Homicide, UN Office on Drugs and Crime (UNODC). Indicator VC.IHR.PSRC.FE.P5 (https://data.worldbank.org/indicator/VC.IHR.PSRC.FE.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/VC.IHR.PSRC.FE.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 female", "display": { "unit": "per 100,000 female" } }, "vc_ihr_psrc_ma_p5": { "title": "Intentional homicides, male (per 100,000 male)", "description_from_producer": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury.\n\n### Aggregation method:\nAggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf\n\n### Statistical concept and methodology:\nMethodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual.\n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation.\n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States.\n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs.\nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS.\n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury.\n\nThe statistical definition contains three elements that characterize the killing of a person as \u201cintentional homicide\u201d:\n1. The killing of a person by another person (objective element)\n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element)\n3. The unlawfulness of the killing (legal element)\n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide.\n\n### Development relevance:\nIn some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, are making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict.\n\n### Limitations and exceptions:\nStatistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data.", "origins": [ { "producer": "UN Office on Drugs and Crime (UNODC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNODC Research - Data Portal \u2013 Intentional Homicide, UN Office on Drugs and Crime (UNODC). Indicator VC.IHR.PSRC.MA.P5 (https://data.worldbank.org/indicator/VC.IHR.PSRC.MA.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/VC.IHR.PSRC.MA.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 male", "display": { "unit": "per 100,000 male" } }, "vc_ihr_psrc_p5": { "title": "Intentional homicides (per 100,000 people)", "description_from_producer": "An intentional homicide is defined as an unlawful death inflicted upon a person with the intent to cause death or serious injury.\n\n### Aggregation method:\nAggregate values are computed by UNODC. For additional information, please see the UNODC website: https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_intentional_homicide.pdf\n\n### Statistical concept and methodology:\nMethodology: The data are sourced by UNODC from either criminal justice or public health systems. In the former, data are generated by law enforcement or criminal justice authorities in the process of recording and investigating a crime event, whereas in the latter, data are produced by health authorities certifying the cause of death of an individual.\n\nThese data are collected from national authorities with the annual United Nations Survey of Crime Trends and Operations of Criminal Justice Systems (UN-CTS). National focal points working in national agencies responsible for statistics on crime and the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the UN-CTS to UNODC. Following the submission, UNODC checks for consistency and coherence with other data sources. Member States which are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union are sending their response to the UN-CTS to Eurostat for validation.\n\nData submitted by Member States through other means or taken from other sources are added to the dataset after review by Member States.\n\nThe population data is sourced from the World Population Prospect, Population Division, United Nations Department of Economic and Social Affairs.\nStatistical concept(s): The International Classification of Crime for Statistical Purposes (ICCS) is the source of the definition of intentional homicide. The definitions of the disaggregation of victims of intentional homicide included in these tables (by situational context, by relationship to perpetrator and by mechanisms) are also from the ICCS.\n\nThe ICCS includes more information on what is included and excluded in these offences. Intentional homicide (ICCS 0101): Unlawful death inflicted upon a person with the intent to cause death or serious injury.\n\nThe statistical definition contains three elements that characterize the killing of a person as \u201cintentional homicide\u201d:\n1. The killing of a person by another person (objective element)\n2. The intent of the perpetrator to kill or seriously injure the victim (subjective element)\n3. The unlawfulness of the killing (legal element)\n\nFor recording purposes, all killings that meet the criteria listed above are to be considered intentional homicides, irrespective of definitions provided by national legislations or practices. Killings as a result of terrorist activities are also to be classified as a form of intentional homicide.\n\n### Development relevance:\nIn some regions, organized crime, drug trafficking and the violent cultures of youth gangs are predominantly responsible for the high levels of homicide. There has been a sharp increase in homicides in some countries, particularly in Central America, making the activities of organized crime and drug trafficking more visible. Greater use of firearms is often associated with the illicit activities of organized criminal groups, which are often linked to drug trafficking.\n\nKnowledge of the patterns and causes of violent crime are crucial to forming preventive strategies. Young males are the group most affected by violent crime in all regions, particularly in the Americas. Yet women of all ages are the victims of intimate partner and family-related violence in all regions and countries. Indeed, in many of them, it is within the home where a woman is most likely to be killed.\n\nData on intentional homicides are from the United Nations Office on Drugs and Crime (UNODC), which uses a variety of national and international sources on homicides - primarily criminal justice sources as well as public health data from the World Health Organization (WHO) and the Pan American Health Organization - and the United Nations Survey of Crime Trends and Operations of Criminal Justice Systems to present accurate and comparable statistics. The UNODC defines homicide as \"unlawful death purposefully inflicted on a person by another person.\" This definition excludes deaths arising from armed conflict.\n\n### Limitations and exceptions:\nStatistics reported to the United Nations in the context of its various surveys on crime levels and criminal justice trends are incidents of victimization that have been reported to the authorities in any given country. That means that this data is subject to the problems of accuracy of all official crime data. The survey results provide an overview of trends and interrelationships between various parts of the criminal justice system to promote informed decision-making in administration, nationally and internationally.\n\nThe degree to which different societies apportion the level of culpability to acts resulting in death is also subject to variation. Consequently, the comparison between countries and regions of \"intentional homicide\", or unlawful death purposefully inflicted on a person by another person, is also a comparison of the extent to which different countries deem that a killing be classified as such, as well as the capacity of their legal systems to record it. Caution should therefore be applied when evaluating and comparing homicide data.", "origins": [ { "producer": "UN Office on Drugs and Crime (UNODC), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "UNODC Research - Data Portal \u2013 Intentional Homicide, UN Office on Drugs and Crime (UNODC). Indicator VC.IHR.PSRC.P5 (https://data.worldbank.org/indicator/VC.IHR.PSRC.P5). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/VC.IHR.PSRC.P5", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "per 100,000 people", "display": { "unit": "per 100,000 people" } }, "per_allsp_adq_pop_tot": { "title": "Adequacy of social protection and labor programs (% of total welfare of beneficiary households)", "description_from_producer": "Adequacy of social protection and labor programs (SPL) is measured by the total transfer amount received by the population participating in social insurance, social safety net, and unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_allsp.adq_pop_tot (https://data.worldbank.org/indicator/per_allsp.adq_pop_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_allsp.adq_pop_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total welfare of beneficiary households", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total welfare of beneficiary households" } }, "per_allsp_ben_q1_tot": { "title": "Benefit incidence of social protection and labor programs to poorest quintile (% of total SPL benefits)", "description_from_producer": "Benefit incidence of social protection and labor programs (SPL) to poorest quintile shows the percentage of total social protection and labor programs benefits received by the poorest 20% of the population. Social protection and labor programs include social insurance, social safety nets, and unemployment benefits and active labor market programs. Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_allsp.ben_q1_tot (https://data.worldbank.org/indicator/per_allsp.ben_q1_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_allsp.ben_q1_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total SPL benefits", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total SPL benefits" } }, "per_allsp_cov_pop_tot": { "title": "Coverage of social protection and labor programs (% of population)", "description_from_producer": "Coverage of social protection and labor programs (SPL) shows the percentage of population participating in social insurance, social safety net, and unemployment benefits and active labor market programs. Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_allsp.cov_pop_tot (https://data.worldbank.org/indicator/per_allsp.cov_pop_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_allsp.cov_pop_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_lm_alllm_adq_pop_tot": { "title": "Adequacy of unemployment benefits and ALMP (% of total welfare of beneficiary households)", "description_from_producer": "Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_lm_alllm.adq_pop_tot (https://data.worldbank.org/indicator/per_lm_alllm.adq_pop_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_lm_alllm.adq_pop_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total welfare of beneficiary households", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total welfare of beneficiary households" } }, "per_lm_alllm_ben_q1_tot": { "title": "Benefit incidence of unemployment benefits and ALMP to poorest quintile (% of total U/ALMP benefits)", "description_from_producer": "Benefit incidence of unemployment benefits and active labor market programs (ALMP) to poorest quintile shows the percentage of total unemployment and active labor market programs benefits received by the poorest 20% of the population. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_lm_alllm.ben_q1_tot (https://data.worldbank.org/indicator/per_lm_alllm.ben_q1_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_lm_alllm.ben_q1_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total U/ALMP benefits", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total U/ALMP benefits" } }, "per_lm_alllm_cov_pop_tot": { "title": "Coverage of unemployment benefits and ALMP (% of population)", "description_from_producer": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_lm_alllm.cov_pop_tot (https://data.worldbank.org/indicator/per_lm_alllm.cov_pop_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_lm_alllm.cov_pop_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_lm_alllm_cov_q1_tot": { "title": "Coverage of unemployment benefits and ALMP in poorest quintile (% of population)", "description_from_producer": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_lm_alllm.cov_q1_tot (https://data.worldbank.org/indicator/per_lm_alllm.cov_q1_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_lm_alllm.cov_q1_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_lm_alllm_cov_q2_tot": { "title": "Coverage of unemployment benefits and ALMP in 2nd quintile (% of population)", "description_from_producer": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_lm_alllm.cov_q2_tot (https://data.worldbank.org/indicator/per_lm_alllm.cov_q2_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_lm_alllm.cov_q2_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_lm_alllm_cov_q3_tot": { "title": "Coverage of unemployment benefits and ALMP in 3rd quintile (% of population)", "description_from_producer": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_lm_alllm.cov_q3_tot (https://data.worldbank.org/indicator/per_lm_alllm.cov_q3_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_lm_alllm.cov_q3_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_lm_alllm_cov_q4_tot": { "title": "Coverage of unemployment benefits and ALMP in 4th quintile (% of population)", "description_from_producer": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_lm_alllm.cov_q4_tot (https://data.worldbank.org/indicator/per_lm_alllm.cov_q4_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_lm_alllm.cov_q4_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_lm_alllm_cov_q5_tot": { "title": "Coverage of unemployment benefits and ALMP in richest quintile (% of population)", "description_from_producer": "Coverage of unemployment benefits and active labor market programs (ALMP) shows the percentage of population participating in unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_lm_alllm.cov_q5_tot (https://data.worldbank.org/indicator/per_lm_alllm.cov_q5_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_lm_alllm.cov_q5_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_sa_allsa_adq_pop_tot": { "title": "Adequacy of social safety net programs (% of total welfare of beneficiary households)", "description_from_producer": "Adequacy of social safety net programs is measured by the total transfer amount received by the population participating in social safety net programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Social safety net programs include cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_sa_allsa.adq_pop_tot (https://data.worldbank.org/indicator/per_sa_allsa.adq_pop_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_sa_allsa.adq_pop_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total welfare of beneficiary households", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total welfare of beneficiary households" } }, "per_sa_allsa_ben_q1_tot": { "title": "Benefit incidence of social safety net programs to poorest quintile (% of total safety net benefits)", "description_from_producer": "Benefit incidence of social safety net programs to poorest quintile shows the percentage of total social safety net benefits received by the poorest 20% of the population. Social safety net programs include cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_sa_allsa.ben_q1_tot (https://data.worldbank.org/indicator/per_sa_allsa.ben_q1_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_sa_allsa.ben_q1_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total safety net benefits", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total safety net benefits" } }, "per_sa_allsa_cov_pop_tot": { "title": "Coverage of social safety net programs (% of population)", "description_from_producer": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_sa_allsa.cov_pop_tot (https://data.worldbank.org/indicator/per_sa_allsa.cov_pop_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_sa_allsa.cov_pop_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_sa_allsa_cov_q1_tot": { "title": "Coverage of social safety net programs in poorest quintile (% of population)", "description_from_producer": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_sa_allsa.cov_q1_tot (https://data.worldbank.org/indicator/per_sa_allsa.cov_q1_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_sa_allsa.cov_q1_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_sa_allsa_cov_q2_tot": { "title": "Coverage of social safety net programs in 2nd quintile (% of population)", "description_from_producer": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_sa_allsa.cov_q2_tot (https://data.worldbank.org/indicator/per_sa_allsa.cov_q2_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_sa_allsa.cov_q2_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_sa_allsa_cov_q3_tot": { "title": "Coverage of social safety net programs in 3rd quintile (% of population)", "description_from_producer": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_sa_allsa.cov_q3_tot (https://data.worldbank.org/indicator/per_sa_allsa.cov_q3_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_sa_allsa.cov_q3_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_sa_allsa_cov_q4_tot": { "title": "Coverage of social safety net programs in 4th quintile (% of population)", "description_from_producer": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_sa_allsa.cov_q4_tot (https://data.worldbank.org/indicator/per_sa_allsa.cov_q4_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_sa_allsa.cov_q4_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_sa_allsa_cov_q5_tot": { "title": "Coverage of social safety net programs in richest quintile (% of population)", "description_from_producer": "Coverage of social safety net programs shows the percentage of population participating in cash transfers and last resort programs, noncontributory social pensions, other cash transfers programs (child, family and orphan allowances, birth and death grants, disability benefits, and other allowances), conditional cash transfers, in-kind food transfers (food stamps and vouchers, food rations, supplementary feeding, and emergency food distribution), school feeding, other social assistance programs (housing allowances, scholarships, fee waivers, health subsidies, and other social assistance) and public works programs (cash for work and food for work). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_sa_allsa.cov_q5_tot (https://data.worldbank.org/indicator/per_sa_allsa.cov_q5_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_sa_allsa.cov_q5_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_si_allsi_adq_pop_tot": { "title": "Adequacy of social insurance programs (% of total welfare of beneficiary households)", "description_from_producer": "Adequacy of social insurance programs is measured by the total transfer amount received by the population participating in social insurance programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Social insurance programs include old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_si_allsi.adq_pop_tot (https://data.worldbank.org/indicator/per_si_allsi.adq_pop_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_si_allsi.adq_pop_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total welfare of beneficiary households", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total welfare of beneficiary households" } }, "per_si_allsi_ben_q1_tot": { "title": "Benefit incidence of social insurance programs to poorest quintile (% of total social insurance benefits)", "description_from_producer": "Benefit incidence of social insurance programs to poorest quintile shows the percentage of total social insurance benefits received by the poorest 20% of the population. Social insurance programs include old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_si_allsi.ben_q1_tot (https://data.worldbank.org/indicator/per_si_allsi.ben_q1_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_si_allsi.ben_q1_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of total social insurance benefits", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of total social insurance benefits" } }, "per_si_allsi_cov_pop_tot": { "title": "Coverage of social insurance programs (% of population)", "description_from_producer": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_si_allsi.cov_pop_tot (https://data.worldbank.org/indicator/per_si_allsi.cov_pop_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_si_allsi.cov_pop_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_si_allsi_cov_q1_tot": { "title": "Coverage of social insurance programs in poorest quintile (% of population)", "description_from_producer": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_si_allsi.cov_q1_tot (https://data.worldbank.org/indicator/per_si_allsi.cov_q1_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_si_allsi.cov_q1_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_si_allsi_cov_q2_tot": { "title": "Coverage of social insurance programs in 2nd quintile (% of population)", "description_from_producer": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_si_allsi.cov_q2_tot (https://data.worldbank.org/indicator/per_si_allsi.cov_q2_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_si_allsi.cov_q2_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_si_allsi_cov_q3_tot": { "title": "Coverage of social insurance programs in 3rd quintile (% of population)", "description_from_producer": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_si_allsi.cov_q3_tot (https://data.worldbank.org/indicator/per_si_allsi.cov_q3_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_si_allsi.cov_q3_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_si_allsi_cov_q4_tot": { "title": "Coverage of social insurance programs in 4th quintile (% of population)", "description_from_producer": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_si_allsi.cov_q4_tot (https://data.worldbank.org/indicator/per_si_allsi.cov_q4_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_si_allsi.cov_q4_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "per_si_allsi_cov_q5_tot": { "title": "Coverage of social insurance programs in richest quintile (% of population)", "description_from_producer": "Coverage of social insurance programs shows the percentage of population participating in programs that provide old age contributory pensions (including survivors and disability) and social security and health insurance benefits (including occupational injury benefits, paid sick leave, maternity and other social insurance). Estimates include both direct and indirect beneficiaries.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: ASPIRE performance indicators are generally based on national representative household surveys (except for Argentina where the survey is only urban representative) including household income expenditure/budget surveys, Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring Surveys. Efforts are made to ensure that welfare aggregates (either income or consumption per capita) used to rank households are those harmonized by World Bank regional poverty teams and are up-to-date.\nStatistical concept(s): Population Coverage Rate\n\n### Development relevance:\nBy harmonizing survey data for 129 countries, ASPIRE aims to meet the increasing demand on comparable and up-to-date SPL data from policymakers, practitioners and other country stakeholders, World Bank staff, other development organizations, researchers and civil society.\n\n\n\nIn summary, ASPIRE indicators based on household surveys are useful for:\n\n\n\nBenchmarking SPL programs and systems performance in terms of overall coverage, benefit incidence, adequacy of benefits, impact on poverty and inequality, benefit-cost ratios as well as programs overlaps and spending. These indicators are provided by program type, by extreme poor, poor and non-poor, by quintiles of (before and after-transfer) welfare distribution, and by urban/rural geographical areas.\n\nComplementing administrative data on SPL programs and systems collected through countries\u2019 Management Information Systems for a broader analysis.\n\nProviding a description of country SPL systems based on nationally representative household surveys (with related caveats) and on information directly collected from beneficiaries.\n\n### Limitations and exceptions:\nWhen interpreting ASPIRE performance indicators based on household surveys, it is important to note that the extent to which information on specific transfers and programs is captured in the household surveys can vary a lot across countries. Moreover, household surveys do not capture the universe of social protection programs in the country, in best practice cases just the largest programs. As a consequence, ASPIRE indicators are not fully comparable across program categories and countries; however, they provide approximate measures of social protection systems performance. In addition, there may be cases where ASPIRE performance indicators differ from official WB country reports as ASPIRE indicators are based on a first level analysis of original survey data and unified methodology that does not necessarily reflect country-specific knowledge and in depth country analysis relying on administrative program level data and/or imputations.", "origins": [ { "producer": "ASPIRE: The Atlas of Social Protection, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, World Bank (WB), uri: datatopics.worldbank.org/aspire/, note: Data are based on national representative household surveys. Indicator per_si_allsi.cov_q5_tot (https://data.worldbank.org/indicator/per_si_allsi.cov_q5_tot). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/per_si_allsi.cov_q5_tot", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" } }, "omm_urb_pop_slum": { "title": "Urban population living in slums", "description": "Total urban population living in slums. This variable is calculated by Our World in Data based on the following variables from United Nations Population Division, national statistical offices, and Eurostat, via World Bank: \"Urban population\" and \"Population living in slums (% of urban population)\"\n\n----\nUrban population: \n\n----\nPopulation living in slums (% of urban population): ", "origins": [ { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank - World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "", "short_unit": "" }, "omm_urb_pop_nonslum": { "title": "Urban population not living in slums", "description": "Total urban population not living in slums. This variable is calculated by Our World in Data based on the following variables from United Nations Population Division, national statistical offices, and Eurostat, via World Bank: \"Urban population\" and \"Population living in slums (% of urban population)\"\n\n----\nUrban population: \n\n----\nPopulation living in slums (% of urban population): ", "origins": [ { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank - World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "", "short_unit": "" }, "omm_share_service_exports": { "title": "Share of services in total goods and services exports", "description": "Service exports as a share of total exports of goods and services. This variable is calculated by Our World in Data based on the following variables from United Nations Population Division, national statistical offices, and Eurostat, via World Bank: \"Service exports (BoP, current US$)\" and \"Goods exports (BoP, current US$)\"\n\n----\nService exports (BoP, current US$): \n\n----\nGoods exports (BoP, current US$): ", "origins": [ { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank - World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of goods and services exports", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of goods and services exports" } }, "omm_merch_exp_share_gdp": { "title": "Merchandise exports as a share of GDP", "description": "Merchandise exports as a share of GDP. This variable is calculated by Our World in Data based on the following variables from United Nations Population Division, national statistical offices, and Eurostat, via World Bank: \"Merchandise exports (current US$)\" and \"GDP (current US$)\"\n\n----\nMerchandise exports (current US$): \n\n----\nGDP (current US$): ", "origins": [ { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank - World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "omm_goods_exp_share_gdp": { "title": "Goods exports as a share of GDP", "description": "Goods exports as a share of GDP. This variable is calculated by Our World in Data based on the following variables from United Nations Population Division, national statistical offices, and Eurostat, via World Bank: \"Goods exports (BoP, current US$)\" and \"GDP (current US$)\"\n\n----\nGoods exports (BoP, current US$): \n\n----\nGDP (current US$): ", "origins": [ { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank - World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of GDP", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of GDP" } }, "omm_tax_rev_percap": { "title": "Tax revenues per capita (current international $)", "description": "Tax revenues per capita, expressed in current international $. This variable is calculated by Our World in Data based on the following variables from United Nations Population Division, national statistical offices, and Eurostat, via World Bank: \"Tax revenue (% of GDP)\" and \"GDP per capita, PPP (current international $)\"\n\n----\nTax revenue (% of GDP): \n\n----\nGDP per capita, PPP (current international $): ", "origins": [ { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank - World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current international $", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current international $" } }, "omm_net_savings_percap": { "title": "Adjusted net savings per capita, excluding particulate emission damage (current US$)", "description": "Adjusted net savings per capita (excluding particulate emission damage), expressed in current US$. This variable is calculated by Our World in Data based on the following variables from United Nations Population Division, national statistical offices, and Eurostat, via World Bank: \"Adjusted net savings, excluding particulate emission damage (current US$)\" and \"Population, total\"\n\n----\nAdjusted net savings, excluding particulate emission damage (current US$): \n\n----\nPopulation, total: ", "origins": [ { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank - World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "armed_forces_share_population": { "title": "Armed forces personnel (% of total population)", "description_from_producer": "Armed forces personnel are active duty military personnel, including paramilitary forces if the training, organization, equipment, and control suggest they may be used to support or replace regular military forces.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: Military data on manpower represent quantitative assessment of the personnel strengths of the world's armed forces. The IISS collects the data from a wide variety of sources. The numbers are based on the most accurate data available to, or on the best estimate that can be made by the International Institute for Strategic Studies (IISS) at the time of its annual publication. The current WDI indicator includes active armed forces and active paramilitary (but not reservists). Armed forces personnel comprise all servicemen and women on full-time duty, including conscripts and long-term assignments from the Reserves (\u201cReserve\u201d describes formations and units not fully manned or operational in peacetime, but which can be mobilized by recalling reservists in an emergency). The indicator includes paramilitary forces. The source of the data (IISS) reports armed forces and paramilitary forces separately, however these figures are added for the purpose of computing this series. Home Guard units are counted as paramilitary.\n\n### Development relevance:\nAlthough national defense is an important function of government and security from external threats that contributes to economic development, high military expenditures for defense or civil conflicts burden the economy and may impede growth. Data on military expenditures are a rough indicator of the portion of national resources used for military activities and of the burden on the economy.\n\nComparisons of military spending among countries should take into account the many factors that influence perceptions of vulnerability and risk, including historical and cultural traditions, the length of borders that need defending, the quality of relations with neighbors, and the role of the armed forces in the body politic.\n\n### Limitations and exceptions:\nData excludes personnel not on active duty, therefore it underestimates the share of the labor force working for the defense establishment. The cooperation of governments of all countries listed in \u201cThe Military Balance\u201d has been sought by IISS and, in many cases, received. However, some data in \u201cThe Military Balance\u201d is estimated.\n\n### Other notes:\nData for some countries are based on partial or uncertain data or rough estimates.", "origins": [ { "producer": "The Military Balance - International Institute for Strategic Studies, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "The Military Balance, International Institute for Strategic Studies. Indicator MS.MIL.TOTL.P1 (https://data.worldbank.org/indicator/MS.MIL.TOTL.P1). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/MS.MIL.TOTL.P1", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "Various sources", "title": "Population", "description": "Our World in Data builds and maintains a long-run dataset on population by country, region, and for the world, based on various sources.\n\nYou can find more information on these sources and how our time series is constructed on this page: https://ourworldindata.org/population-sources", "citation_full": "The long-run data on population is based on various sources, described on this page: https://ourworldindata.org/population-sources", "attribution": "Population based on various sources (2024)", "attribution_short": "Population", "url_main": "https://ourworldindata.org/population-sources", "date_accessed": "2026-03-31", "date_published": "2024-07-15", "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/" } } ], "unit": "%", "short_unit": "%", "display": { "numDecimalPlaces": 1, "shortUnit": "%", "unit": "%" }, "processing_level": "minor", "description_processing": "We estimated this indicator by dividing the total number of armed forces personnel by the total population. The population estimates come from a long-run dataset [maintained by Our World in Data](https://ourworldindata.org/population-sources)." }, "si_rmt_cost_ib_zs": { "title": "Average transaction cost of sending remittances to a specific country (%)", "origins": [ { "producer": "Remittance Prices Worldwide, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Remittance Prices Worldwide, World Bank (WB), uri: http://remittanceprices.worldbank.org. Indicator SI.RMT.COST.IB.ZS (https://data.worldbank.org/indicator/SI.RMT.COST.IB.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.RMT.COST.IB.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "World Bank based on International Monetary Fund balance of payments data", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IMF balance of payments data, International Monetary Fund (IMF);\nStaff estimates, World Bank (WB). Indicator BX.TRF.PWKR.CD.DT (https://data.worldbank.org/indicator/BX.TRF.PWKR.CD.DT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.TRF.PWKR.CD.DT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" }, "description_processing": "We calculated the average transaction cost for sending money to a region by calculating total volume of incoming transactions and the total associated costs (assuming average transaction costs). We then divided the total costs by the total volume of transactions to get the average transaction cost. This indicator is only available if cost data is available for more than 75% of the total volume of transactions." }, "si_rmt_cost_ob_zs": { "title": "Average transaction cost of sending remittances from a specific country (%)", "origins": [ { "producer": "Remittance Prices Worldwide, World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Remittance Prices Worldwide, World Bank (WB), uri: http://remittanceprices.worldbank.org. Indicator SI.RMT.COST.OB.ZS (https://data.worldbank.org/indicator/SI.RMT.COST.OB.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SI.RMT.COST.OB.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "World Bank based on International Monetary Fund balance of payments data", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IMF balance of payments data, International Monetary Fund (IMF);\nStaff estimates, World Bank (WB). Indicator BM.TRF.PWKR.CD.DT (https://data.worldbank.org/indicator/BM.TRF.PWKR.CD.DT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.TRF.PWKR.CD.DT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "%", "short_unit": "%", "display": { "shortUnit": "%", "unit": "%" }, "description_processing": "We calculated the average transaction cost for sending money from a region by calculating total volume of outgoing transactions and the total associated costs (assuming average transaction costs). We then divided the total costs by the total volume of transactions to get the average transaction cost. This indicator is only available if cost data is available for more than 75% of the total volume of transactions." }, "bx_trf_pwkr_cd_dt": { "title": "Personal remittances, received (current US$)", "description_from_producer": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The two main components of personal remittances, \"personal transfers\" and \"compensation of employees\", are items in the balance of payments (BPM6) framework. Both of these standard components are recorded in the current account.\n\"Personal transfers\", a new item in the Balance of Payments (BPM6) represents a broader definition of worker remittances. Personal transfers include all current transfers in cash or in kind between resident and nonresident individuals, independent of the source of income of the sender (irrespective of whether the sender receives income from labor, entrepreneurial or property income, social benefits, and any other types of transfers; or disposes assets) and the relationship between the households (irrespective of whether they are related or unrelated individuals).\n\nCompensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Compensation of employees represents remuneration in return for the labor input to the production process contributed by an individual in an employer-employee relationship with the enterprise. Compensation of employees is recorded gross and includes amounts paid by the employee as taxes or for other purposes in the economy where the work is performed. Compensation of employees has three main components: wages and salaries in cash, wages and salaries in kind, and employers' social contributions.\n\n### Development relevance:\nMovement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nIn most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. Globally, the number of refugees at end 2010 was 10.55 million, including 597,300 people considered by the United Nations High Commissioner for Refugees (UNHCR) to be in a refugee-like situation; developing countries hosted 8.5 million refugees, or 80 percent of the global refugee population.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights.\n\n### Limitations and exceptions:\nRemittance transactions have grown in importance over the past decade. In a number of developing economies, receipts of remittances have become an important and stable source of funds that exceeds receipts from exports of goods and services or from financial inflows on foreign direct investment. But the quality of statistical remittance data is not high. Remittances are a challenge to measure because of their nature. They are heterogeneous with numerous small transactions conducted by individuals through a wide variety of channels: formal channels, such as electronic wire, or through informal channels, such as cash or goods carried across borders. The large number of remittance transactions and the multitude of channels pose challenges to the compilation of comprehensive statistics. The small size of individual transactions means that they often go undetected by typical data source systems, although the aggregate level of transactions may be substantial.\n\nBecause of difficulties in obtaining data on informal remittance transactions, the remittance transactions undertaken through informal channels are sometimes not well covered in current balance of payments data. As a result, even though direct measurement of remittances - through transactions reporting or surveys - may be considered preferable if feasible, some countries instead combine different sources and estimation methods to achieve better coverage, by using direct measurements where practical and supplemented estimates where they are not. Model-based approaches are used in some countries as they are flexible. Compilers can design models to fill gaps in data sources or to provide global totals.\n\nHowever, only reliable input data can lead to sound estimates, regardless of the sophistication of an estimation method or econometric model. Indirect data are converted to remittance estimates using a set of assumptions. These assumptions should be plausible, but it is often not possible to test or verify these assumptions and also the results in practice.\n\n### Other notes:\nNote: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6).", "origins": [ { "producer": "World Bank based on International Monetary Fund balance of payments data", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IMF balance of payments data, International Monetary Fund (IMF);\nStaff estimates, World Bank (WB). Indicator BX.TRF.PWKR.CD.DT (https://data.worldbank.org/indicator/BX.TRF.PWKR.CD.DT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BX.TRF.PWKR.CD.DT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "bm_trf_pwkr_cd_dt": { "title": "Personal remittances, paid (current US$)", "description_from_producer": "Personal remittances comprise personal transfers and compensation of employees. Personal transfers consist of all current transfers in cash or in kind made or received by resident households to or from nonresident households. Personal transfers thus include all current transfers between resident and nonresident individuals. Compensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Data are the sum of two items defined in the sixth edition of the IMF's Balance of Payments Manual: personal transfers and compensation of employees. Data are in current U.S. dollars.\n\n### Aggregation method:\nSum\n\n### Statistical concept and methodology:\nMethodology: The two main components of personal remittances, \"personal transfers\" and \"compensation of employees\", are items in the balance of payments (BPM6) framework. Both of these standard components are recorded in the current account.\n\"Personal transfers,\" a new item in the Balance of Payments (BPM6) represents a broader definition of worker remittances. Personal transfers include all current transfers in cash or in kind between resident and nonresident individuals, independent of the source of income of the sender (irrespective of whether the sender receives income from labor, entrepreneurial or property income, social benefits, and any other types of transfers; or disposes assets) and the relationship between the households (irrespective of whether they are related or unrelated individuals).\n\nCompensation of employees refers to the income of border, seasonal, and other short-term workers who are employed in an economy where they are not resident and of residents employed by nonresident entities. Compensation of employees represents remuneration in return for the labor input to the production process contributed by an individual in an employer-employee relationship with the enterprise. Compensation of employees is recorded gross and includes amounts paid by the employee as taxes or for other purposes in the economy where the work is performed. Compensation of employees has three main components: wages and salaries in cash, wages and salaries in kind, and employers' social contributions.\n\n### Development relevance:\nMovement of people, most often through migration, is a significant part of global integration. Migrants contribute to the economies of both their host country and their country of origin. Yet reliable statistics on migration are difficult to collect and are often incomplete, making international comparisons a challenge.\n\nIn most developed countries, refugees are admitted for resettlement and are routinely included in population counts by censuses or population registers. Globally, the number of refugees at end 2010 was 10.55 million, including 597,300 people considered by the United Nations High Commissioner for Refugees (UNHCR) to be in a refugee-like situation; developing countries hosted 8.5 million refugees, or 80 percent of the global refugee population.\n\nGlobal migration patterns have become increasingly complex in modern times, involving not just refugees, but also millions of economic migrants. But refugees and migrants, even if they often travel in the same way, are fundamentally different, and for that reason are treated very differently under modern international law. Migrants, especially economic migrants, choose to move in order to improve the future prospects of themselves and their families. Refugees have to move if they are to save their lives or preserve their freedom. They have no protection from their own state - indeed it is often their own government that is threatening to persecute them. If other countries do not let them in, and do not help them once they are in, then they may be condemning them to death - or to an intolerable life in the shadows, without sustenance and without rights.\n\n### Limitations and exceptions:\nRemittance transactions have grown in importance over the past decade. In a number of developing economies, receipts of remittances have become an important and stable source of funds that exceeds receipts from exports of goods and services or from financial inflows on foreign direct investment. But the quality of statistical remittance data is not high. Remittances are a challenge to measure because of their nature. They are heterogeneous with numerous small transactions conducted by individuals through a wide variety of channels: formal channels, such as electronic wire, or through informal channels, such as cash or goods carried across borders. The large number of remittance transactions and the multitude of channels pose challenges to the compilation of comprehensive statistics. The small size of individual transactions means that they often go undetected by typical data source systems, although the aggregate level of transactions may be substantial.\n\nBecause of difficulties in obtaining data on informal remittance transactions, the remittance transactions undertaken through informal channels are sometimes not well covered in current balance of payments data. As a result, even though direct measurement of remittances - through transactions reporting or surveys - may be considered preferable if feasible, some countries instead combine different sources and estimation methods to achieve better coverage, by using direct measurements where practical and supplemented estimates where they are not. Model-based approaches are used in some countries as they are flexible. Compilers can design models to fill gaps in data sources or to provide global totals.\n\nHowever, only reliable input data can lead to sound estimates, regardless of the sophistication of an estimation method or econometric model. Indirect data are converted to remittance estimates using a set of assumptions. These assumptions should be plausible, but it is often not possible to test or verify these assumptions and also the results in practice.\n\n### Other notes:\nNote: Data starting from 2005 are based on the sixth edition of the IMF's Balance of Payments Manual (BPM6).", "origins": [ { "producer": "World Bank based on International Monetary Fund balance of payments data", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "IMF balance of payments data, International Monetary Fund (IMF);\nStaff estimates, World Bank (WB). Indicator BM.TRF.PWKR.CD.DT (https://data.worldbank.org/indicator/BM.TRF.PWKR.CD.DT). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/BM.TRF.PWKR.CD.DT", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "current US$", "short_unit": "$", "display": { "shortUnit": "$", "unit": "current US$" } }, "ny_gdp_mktp_cd_adjusted": { "title": "GDP (constant 2021 US$)", "description": "Population by country and year.", "description_short": "Total economic output of a country or region per year. This data is adjusted by inflation but _does not_ account for differences in living costs between countries.", "description_key": "- [Gross domestic product](#dod:gdp) (GDP) is a comprehensive measure of a country's total income. It helps compare the size of economies and track how they change over time. It is especially useful for understanding trends in economic growth.\n- The GDP is calculated as the value of all final goods and services produced each year in a country. It represents the total economic output of a country or region.\n- This GDP indicator provides information on economic growth and income levels from 1960.\n- This data is adjusted by inflation but _does not_ account for differences in living costs between countries.\n- This data is expressed in constant 2021 US$.\n- For GDP estimates that account for both inflation and differences in the cost of living between countries, see [GDP in constant international-$](https://ourworldindata.org/grapher/gdp-worldbank).", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.MKTP.CD (https://data.worldbank.org/indicator/NY.GDP.MKTP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.MKTP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.MKTP.KN (https://data.worldbank.org/indicator/NY.GDP.MKTP.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.MKTP.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "Various sources", "title": "Population", "description": "Our World in Data builds and maintains a long-run dataset on population by country, region, and for the world, based on various sources.\n\nYou can find more information on these sources and how our time series is constructed on this page: https://ourworldindata.org/population-sources", "citation_full": "The long-run data on population is based on various sources, described on this page: https://ourworldindata.org/population-sources", "attribution": "Population based on various sources (2024)", "attribution_short": "Population", "url_main": "https://ourworldindata.org/population-sources", "date_accessed": "2026-03-31", "date_published": "2024-07-15", "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/" } } ], "unit": "constant 2021 US$", "short_unit": "$", "display": { "name": "GDP", "numDecimalPlaces": 0, "tolerance": 5, "shortUnit": "$", "unit": "constant 2021 US$" }, "processing_level": "major", "presentation": { "title_public": "Gross domestic product (GDP)", "title_variant": "In constant 2021 US$", "attribution_short": "World Bank", "topic_tags": [ "Economic Growth" ] }, "description_processing": "We constructed estimates of GDP and GDP per capita in constant 2021 US$ by taking the corresponding series in current US$ and applying them the growth rates of the series in constant local currency units, adjusted by the base year (2021). This is [the same methodology](https://datahelpdesk.worldbank.org/knowledgebase/articles/114943-how-do-you-derive-constant-price-series-in-usd) followed by the World Bank to obtain the series in constant 2015 US$.\n\nWe obtain this indicator to have a more meaningful comparison between exchange-rate-based and PPP-based GDP estimates in the same reference year.\n\nWe have also obtained [Our World in Data regional aggregations](https://ourworldindata.org/world-region-map-definitions) of these indicators by calculating a population-weighted average by region. You can see more information about the population data in [this dedicated article about sources](https://ourworldindata.org/population-sources)." }, "ny_gdp_pcap_cd_adjusted": { "title": "GDP per capita (constant 2021 US$)", "description": "Population by country and year.", "description_short": "Average economic output per person in a country or region per year. This data is adjusted by inflation but _does not_ account for differences in living costs between countries.", "description_key": "- Gross domestic product (GDP) is a measure of the total value added from the production of goods and services in a country or region each year. GDP per capita is GDP divided by population.\n- This GDP per capita indicator provides information on economic growth and income levels from 1960.\n- This data is adjusted by inflation but _does not_ account for differences in living costs between countries.\n- This data is expressed in constant 2021 US$.\n- For GDP per capita estimates that account for both inflation and differences in the cost of living between countries, see [GDP per capita in constant international-$](https://ourworldindata.org/grapher/gdp-per-capita-worldbank).", "origins": [ { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.PCAP.CD (https://data.worldbank.org/indicator/NY.GDP.PCAP.CD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.PCAP.CD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "National statistical organizations and central banks, OECD national accounts, and World Bank staff estimates", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Country official statistics, National Statistical Organizations and/or Central Banks;\nNational Accounts data files, Organisation for Economic Co-operation and Development (OECD);\nStaff estimates, World Bank (WB). Indicator NY.GDP.PCAP.KN (https://data.worldbank.org/indicator/NY.GDP.PCAP.KN). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/NY.GDP.PCAP.KN", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "Various sources", "title": "Population", "description": "Our World in Data builds and maintains a long-run dataset on population by country, region, and for the world, based on various sources.\n\nYou can find more information on these sources and how our time series is constructed on this page: https://ourworldindata.org/population-sources", "citation_full": "The long-run data on population is based on various sources, described on this page: https://ourworldindata.org/population-sources", "attribution": "Population based on various sources (2024)", "attribution_short": "Population", "url_main": "https://ourworldindata.org/population-sources", "date_accessed": "2026-03-31", "date_published": "2024-07-15", "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/" } } ], "unit": "constant 2021 US$", "short_unit": "$", "display": { "name": "GDP per capita", "numDecimalPlaces": 0, "tolerance": 5, "shortUnit": "$", "unit": "constant 2021 US$" }, "processing_level": "major", "presentation": { "title_public": "GDP per capita", "title_variant": "In constant 2021 US$", "attribution_short": "World Bank", "topic_tags": [ "Economic Growth" ] }, "description_processing": "We constructed estimates of GDP and GDP per capita in constant 2021 US$ by taking the corresponding series in current US$ and applying them the growth rates of the series in constant local currency units, adjusted by the base year (2021). This is [the same methodology](https://datahelpdesk.worldbank.org/knowledgebase/articles/114943-how-do-you-derive-constant-price-series-in-usd) followed by the World Bank to obtain the series in constant 2015 US$.\n\nWe obtain this indicator to have a more meaningful comparison between exchange-rate-based and PPP-based GDP estimates in the same reference year.\n\nWe have also obtained [Our World in Data regional aggregations](https://ourworldindata.org/world-region-map-definitions) of these indicators by calculating a population-weighted average by region. You can see more information about the population data in [this dedicated article about sources](https://ourworldindata.org/population-sources)." }, "eg_elc_accs_zs_without": { "title": "Access to electricity (% of population with no access)", "description_short": "Access to electricity means having an electricity source that can provide very basic lighting, and charge a phone or power a radio for 4 hours per day.", "description_from_producer": "Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank\u2019s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021.\n\n### Development relevance:\nMaintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasingly dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indications of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.", "origins": [ { "producer": "SDG 7.1.1 Electrification Dataset, World Bank, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023. Indicator EG.ELC.ACCS.ZS (https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "shortUnit": "%", "unit": "% of population" }, "processing_level": "major", "presentation": { "title_public": "Share of the population with no access to electricity", "attribution_short": "World Bank", "attribution": "Data compiled from multiple sources by the World Bank", "topic_tags": [ "Access to Energy", "Energy" ] }, "description_processing": "We calculated the share of the population without access to electricity by subtracting the share of the population with access from 100%." }, "eg_cft_accs_zs_without": { "title": "Access to clean fuels and technologies for cooking (% of population with no access)", "description_short": "Access to [clean fuels or technologies](#dod:clean-cooking-fuels) such as natural gas, electricity, and clean cookstoves reduces exposure to indoor air pollutants, a leading cause of death in low-income households.", "description_from_producer": "Access to clean fuels and technologies for cooking is the proportion of total population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels.\n\nStatistical concept and methodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization's (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities).\n\nTrends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall \"polluting\" fuels (unprocessed biomass, charcoal, coal, and kerosene) and \"clean\" fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking.", "origins": [ { "producer": "Tracking SDG 7: The Energy Progress Report, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Tracking SDG 7: The Energy Progress Report, ACRA Europe, uri: https://trackingsdg7.esmap.org/, note: License: Creative Commons Attribution\u2014NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO), publisher: International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistical Division (UNSD), World Bank, World Health Organization (WHO), date published: 2025-06;\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report. Indicator EG.CFT.ACCS.ZS (https://data.worldbank.org/indicator/EG.CFT.ACCS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.CFT.ACCS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "% of population", "short_unit": "%", "display": { "name": "Access to clean fuels and technologies for cooking", "shortUnit": "%", "unit": "% of population" }, "processing_level": "major", "presentation": { "title_public": "Share of the population with no access to clean fuels for cooking", "attribution_short": "World Bank", "attribution": "World Health Organization (via World Bank)", "topic_tags": [ "Access to Energy", "Energy" ] }, "description_processing": "We calculated the share of the population without access to clean fuels and technologies for cooking by subtracting the share of the population with access from 100%." }, "eg_elc_accs_zs_number": { "title": "Access to electricity (number of people with access)", "description_short": "Access to electricity means having an electricity source that can provide very basic lighting, and charge a phone or power a radio for 4 hours per day.", "description_from_producer": "Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank\u2019s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021.\n\n### Development relevance:\nMaintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasingly dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indications of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.", "origins": [ { "producer": "SDG 7.1.1 Electrification Dataset, World Bank, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023. Indicator EG.ELC.ACCS.ZS (https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "people", "short_unit": "", "display": { "unit": "people" }, "processing_level": "major", "presentation": { "title_public": "Number of people with access to electricity", "attribution_short": "World Bank", "attribution": "Data compiled from multiple sources by the World Bank", "topic_tags": [ "Access to Energy", "Energy" ] }, "description_processing": "We calculated the number of people with access to electricity by multiplying the fraction of the population with access by the total population." }, "eg_elc_accs_zs_without_number": { "title": "Access to electricity (number of people without access)", "description_short": "Access to electricity means having an electricity source that can provide very basic lighting, and charge a phone or power a radio for 4 hours per day.", "description_from_producer": "Access to electricity is the percentage of population with access to electricity. Electrification data are collected from industry, national surveys and international sources.\n\n### Aggregation method:\nPopulation-weighted average\n\n### Statistical concept and methodology:\nMethodology: The World Bank\u2019s Global Electrification Database (GED) compiles nationally representative household survey data, and occasionally census data, from sources going back as far as 1990. The database also incorporates data from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), Middle East and North Africa Poverty Database (MNAPOV) and the Europe and Central Asia Poverty Database (ECAPOV), which are based on similar surveys. At the time of this analysis, the GED contained 1,375 surveys for 149 countries in 1990-2021.\n\n### Development relevance:\nMaintaining reliable and secure electricity services while seeking to rapidly decarbonize power systems is a key challenge for countries throughout the world. More and more countries are becoming increasingly dependent on reliable and secure electricity supplies to underpin economic growth and community prosperity. This reliance is set to grow as more efficient and less carbon intensive forms of power are developed and deployed to help decarbonize economies.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nEnergy is necessary for creating the conditions for economic growth. It is impossible to operate a factory, run a shop, grow crops or deliver goods to consumers without using some form of energy. Access to electricity is particularly crucial to human development as electricity is, in practice, indispensable for certain basic activities, such as lighting, refrigeration and the running of household appliances, and cannot easily be replaced by other forms of energy. Individuals' access to electricity is one of the most clear and un-distorted indications of a country's energy poverty status.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nElectricity access is increasingly at the forefront of governments' preoccupations, especially in the developing countries. As a consequence, a lot of rural electrification programs and national electrification agencies have been created in these countries to monitor more accurately the needs and the status of rural development and electrification.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nUse of energy is important in improving people's standard of living. But electricity generation also can damage the environment. Whether such damage occurs depends largely on how electricity is generated. For example, burning coal releases twice as much carbon dioxide - a major contributor to global warming - as does burning an equivalent amount of natural gas.", "origins": [ { "producer": "SDG 7.1.1 Electrification Dataset, World Bank, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "SDG 7.1.1 Electrification Dataset, World Bank (WB), uri: https://trackingsdg7.esmap.org/downloads, note: Data is downloaded from ESMAP website. Data is released when a new Tracking SDG7 report is released., publisher: World Bank (WB), date accessed: 2024-05-16, date published: 2023. Indicator EG.ELC.ACCS.ZS (https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "people", "short_unit": "", "display": { "unit": "people" }, "processing_level": "major", "presentation": { "title_public": "Number of people without access to electricity", "attribution_short": "World Bank", "attribution": "Data compiled from multiple sources by the World Bank", "topic_tags": [ "Access to Energy", "Energy" ] }, "description_processing": "We calculated the number of people without access to electricity by multiplying the fraction of the population without access by the total population." }, "eg_cft_accs_zs_number": { "title": "Access to clean fuels and technologies for cooking (number of people with access)", "description_short": "Access to [clean fuels or technologies](#dod:clean-cooking-fuels) such as natural gas, electricity, and clean cookstoves reduces exposure to indoor air pollutants, a leading cause of death in low-income households.", "description_from_producer": "Access to clean fuels and technologies for cooking is the proportion of total population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels.\n\nStatistical concept and methodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization's (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities).\n\nTrends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall \"polluting\" fuels (unprocessed biomass, charcoal, coal, and kerosene) and \"clean\" fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking.", "origins": [ { "producer": "Tracking SDG 7: The Energy Progress Report, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Tracking SDG 7: The Energy Progress Report, ACRA Europe, uri: https://trackingsdg7.esmap.org/, note: License: Creative Commons Attribution\u2014NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO), publisher: International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistical Division (UNSD), World Bank, World Health Organization (WHO), date published: 2025-06;\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report. Indicator EG.CFT.ACCS.ZS (https://data.worldbank.org/indicator/EG.CFT.ACCS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.CFT.ACCS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "people", "short_unit": "", "display": { "name": "Access to clean fuels and technologies for cooking", "unit": "people" }, "processing_level": "major", "presentation": { "title_public": "Number of people with access to clean fuels for cooking", "attribution_short": "World Bank", "attribution": "World Health Organization (via World Bank)", "topic_tags": [ "Access to Energy", "Energy" ] }, "description_processing": "We calculated the number of people with access to clean fuels and technologies for cooking by multiplying the fraction of the population with access by the total population." }, "eg_cft_accs_zs_without_number": { "title": "Access to clean fuels and technologies for cooking (number of people without access)", "description_short": "Access to [clean fuels or technologies](#dod:clean-cooking-fuels) such as natural gas, electricity, and clean cookstoves reduces exposure to indoor air pollutants, a leading cause of death in low-income households.", "description_from_producer": "Access to clean fuels and technologies for cooking is the proportion of total population primarily using clean cooking fuels and technologies for cooking. Under WHO guidelines, kerosene is excluded from clean cooking fuels.\n\nStatistical concept and methodology: Data for access to clean fuels and technologies for cooking are based on the World Health Organization's (WHO) Global Household Energy Database. They are collected among different sources: only data from nationally representative household surveys (including national censuses) were used. Survey sources include Demographic and Health Surveys (DHS) and Living Standards Measurement Surveys (LSMS), Multi-Indicator Cluster Surveys (MICS), the World Health Survey (WHS), other nationally developed and implemented surveys, and various government agencies (for example, ministries of energy and utilities).\n\nTrends in the proportion of the population using each fuel type are estimated using a single multivariate hierarchical model, with urban and rural disaggregation. Estimates for overall \"polluting\" fuels (unprocessed biomass, charcoal, coal, and kerosene) and \"clean\" fuels (gaseous fuels, electricity, as well as an aggregation of any other clean fuels like alcohol) are produced by aggregating estimates of relevant fuel types. The model was used to derive clean fuel use estimates for 191 countries (ref. Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G. and Adair-Rohani, H. (2020), Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. J. R. Stat. Soc. C, 69: 815-839). Countries classified by the World Bank as high income (57 countries) in the 2022 fiscal year are assumed to have universal access to clean fuels and technologies for cooking.", "origins": [ { "producer": "Tracking SDG 7: The Energy Progress Report, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Tracking SDG 7: The Energy Progress Report, ACRA Europe, uri: https://trackingsdg7.esmap.org/, note: License: Creative Commons Attribution\u2014NonCommercial 3.0 IGO (CC BY-NC 3.0 IGO), publisher: International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistical Division (UNSD), World Bank, World Health Organization (WHO), date published: 2025-06;\nInternational Renewable Energy Agency (IRENA), note: Tracking SDG 7: The Energy Progress Report;\nUnited Nations (UN), note: Tracking SDG 7: The Energy Progress Report, publisher: UN Statistics Division;\nWorld Bank (WB), note: Tracking SDG 7: The Energy Progress Report;\nWorld Health Organization (WHO), note: Tracking SDG 7: The Energy Progress Report. Indicator EG.CFT.ACCS.ZS (https://data.worldbank.org/indicator/EG.CFT.ACCS.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/EG.CFT.ACCS.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "people", "short_unit": "", "display": { "name": "Access to clean fuels and technologies for cooking", "unit": "people" }, "processing_level": "major", "presentation": { "title_public": "Number of people without access to clean fuels for cooking", "attribution_short": "World Bank", "attribution": "World Health Organization (via World Bank)", "topic_tags": [ "Access to Energy", "Energy" ] }, "description_processing": "We calculated the number of people without access to clean fuels and technologies for cooking by multiplying the fraction of the population without access by the total population." }, "patents_per_million_people": { "title": "Patent applications per million people", "description_from_producer": "Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention--a product or process that provides a new way of doing something or offers a new technical solution to a problem. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years.\n\nResident patent applications are those for which the first-named applicant or assignee is a resident of the State or region concerned. In the case of regional offices such as the European Patent Office, a resident is an applicant from any of the member States of the regional patent convention. Patent data cover applications and grants classified by field of technology. International applications series distinguish four subcategories: a) patents taken out by residents of a country in that country; b) patents taken out in a country by non-residents of that country; c) total patents registered in the country or naming it; d) patents taken out outside a country by its residents. Data on patents granted only distinguish between patents awarded to residents and to non-residents. A patent provides protection for the invention to the owner of the patent for a limited period, generally 20 years. Patent applications are worldwide patent applications filed through the Patent Cooperation Treaty procedure or with a national patent office for exclusive rights for an invention - a product or process that provides a new way of doing something or offers a new technical solution to a problem.", "description_key": "- Patent applications per million people are calculated by Our World in Data based on patent data from the World Bank's World Development Indicators, and population estimates from the United Nations World Population Prospects.\n- Patents are assigned based on the residence country of the first-named applicant.", "origins": [ { "producer": "World Intellectual Property Organization (WIPO), via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "WIPO Patent Report: Statistics on Worldwide Patent Activity, World Intellectual Property Organization (WIPO), note: The International Bureau of WIPO assumes no responsibility with respect to the transformation of these data. Indicator IP.PAT.RESD (https://data.worldbank.org/indicator/IP.PAT.RESD). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IP.PAT.RESD", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "applications per million people", "short_unit": "", "display": { "numDecimalPlaces": 0, "unit": "applications per million people" }, "presentation": { "topic_tags": [ "Research & Development" ] } }, "articles_per_million_people": { "title": "Scientific and technical journal articles per million people", "description_short": "Scientific and technical journal articles per million people. Disciplines include physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.", "description_from_producer": "Scientific and technical journal articles refer to the number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.\n\nThe number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences. The NSF considers article counts from a set of journals covered by Science Citation Index (SCI) and Social Sciences Citation Index (SSCI).", "description_key": "- Scientific and technical journal articles per million people are calculated by Our World in Data based on article data from the World Bank's World Development Indicators, and population estimates from the United Nations World Population Prospects.\n- Patents are assigned based on the residence country of the first-named applicant.", "origins": [ { "producer": "National Science Foundation Science and Engineering Indicators, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Science and Engineering Indicators, National Science Foundation (NSF), uri: https://ncses.nsf.gov/indicators. Indicator IP.JRN.ARTC.SC (https://data.worldbank.org/indicator/IP.JRN.ARTC.SC). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/IP.JRN.ARTC.SC", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "United Nations Population Division, national statistical offices, and Eurostat, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "World Population Prospects, United Nations (UN), uri: https://population.un.org/wpp/, publisher: UN Population Division;\nStatistical databases and publications from national statistical offices, National Statistical Offices (NSOs), uri: https://unstats.un.org/home/nso_sites/, publisher: National Statistical Offices;\nEurostat: Demographic Statistics, Eurostat (ESTAT), uri: https://ec.europa.eu/eurostat/data/database?node_code=earn_ses_monthly, publisher: Eurostat;\nPopulation and Vital Statistics Report (various years), United Nations (UN), uri: https://unstats.un.org, publisher: UN Statistics Division. Indicator SP.POP.TOTL (https://data.worldbank.org/indicator/SP.POP.TOTL). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SP.POP.TOTL", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "articles per million people", "short_unit": "", "display": { "numDecimalPlaces": 0, "unit": "articles per million people" }, "presentation": { "topic_tags": [ "Research & Development" ] } }, "unemployment_rate_ilo_modeling_comparison_absolute": { "title": "Availability of unemployment estimates by source", "description_short": "Availability and agreement between countries' [national estimates](#dod:ilo-national-estimates) of unemployment rates and the International Labour Organization (ILO) [modeled estimates](#dod:ilo-modelled-estimates).", "description_key": "- This indicator shows how [national](#dod:ilo-national-estimates) and [modeled](#dod:ilo-modelled-estimates) unemployment rate estimates from the International Labour Organization (ILO) compare for each country. It indicates whether the ILO used the same value for national and modeled estimates, a different value, or if the data is only available in one of these datasets. This is obtained by calculating the absolute difference between the two estimates for each country and year. A difference of less than 0.1 percentage points is considered to be the same value.\n- A few countries \u2014 such as Ukraine, Palestine, and Sudan in recent years \u2014 have no published modeled estimates. This usually happens when comparable national data cannot be obtained, or when the ILO considers modeled estimates unreliable, for example, during conflict or major disruption.", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.UEM.TOTL.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "Labour Force Statistics, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "Labour Force Statistics database (LFS), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: June 22, 2026. Indicator SL.UEM.TOTL.NE.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.NE.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.TOTL.NE.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "", "short_unit": "", "display": { "name": "Availability of unemployment estimates by source", "numDecimalPlaces": 0, "tolerance": 0, "type": "categorical", "sort": [ "Both available (agreeing)", "Both available (disagreeing)", "Only ILO modeled available", "Only national estimate available" ] }, "processing_level": "major", "presentation": { "grapher_config": { "note": "When both national survey estimates and ILO modeled estimates are available, they are classified as \"agreeing\" if they differ by less than 0.1 percentage points, and as \"disagreeing\" otherwise." } }, "description_processing": "We derive this indicator by calculating the absolute difference between the ILO modeled estimates and the national estimates for unemployment rate in each country and year. If the absolute difference is less than 0.1 percentage points, we consider the estimates to be the same value. If the absolute difference is 0.1 percentage points or more, we classify it as a different value. If data is only available in one of the datasets, we indicate that as well." }, "number_below_working_age": { "title": "Number of people below working age", "description_short": "Population below working age (under 15 years old).", "description_key": "This data comes from UN World Population Prospects and it is a combination of population estimates and projections to match International Labour Organization data.", "origins": [ { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Estimates 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Medium_1950-2023.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects - Interim Update", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.\n\nThis is an interim update containing revised medium-variant estimates and projections for Togo.", "title_snapshot": "World Population Prospects - Interim Update (2026-01-19, Togo)", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/WPP2024_CSV_files_update.zip", "date_accessed": "2026-03-31", "date_published": "2026-01-19", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Estimates 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_Population1JanuaryBySingleAgeSex_Medium_1950-2023.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Medium 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_Population1JanuaryBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Low-fertility scenario 1950-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Low_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Medium scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, High-fertility scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_High_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Constant-fertility scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Constant%20fertility_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } } ], "unit": "people", "short_unit": "", "display": { "name": "Number of people below working age", "numDecimalPlaces": 0, "tolerance": 0, "unit": "people" }, "presentation": { "topic_tags": [ "Work & Employment" ] } }, "labor_force": { "title": "Labor force", "description_short": "Working-age population (ages 15 and over) who are economically active (employed or unemployed).", "description_from_producer": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.CACT.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.CACT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Estimates 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Medium_1950-2023.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects - Interim Update", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.\n\nThis is an interim update containing revised medium-variant estimates and projections for Togo.", "title_snapshot": "World Population Prospects - Interim Update (2026-01-19, Togo)", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/WPP2024_CSV_files_update.zip", "date_accessed": "2026-03-31", "date_published": "2026-01-19", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Estimates 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_Population1JanuaryBySingleAgeSex_Medium_1950-2023.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Medium 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_Population1JanuaryBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Low-fertility scenario 1950-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Low_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Medium scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, High-fertility scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_High_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Constant-fertility scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Constant%20fertility_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } } ], "unit": "people", "short_unit": "", "display": { "name": "Labor force", "numDecimalPlaces": 0, "tolerance": 0, "unit": "people" }, "processing_level": "major", "presentation": { "topic_tags": [ "Work & Employment" ] }, "description_processing": "We calculated the labor force as the labor force participation rate (coming from ILO Modelled Estimates via World Bank) multiplied by the population over age 15 (coming from UN World Population Prospects)." }, "number_employed": { "title": "Number of employed people", "description_short": "Working-age population (ages 15 and over) who are employed.", "description_from_producer": "Employment to population ratio is the proportion of a country's population that is employed.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The employment-to-population ratio (EPR) is calculated as follows:\n\nEPR (%) = 100 x Persons employed / Working-age population\n\nFor a given component group of the working-age population, the EPR is the percentage of this group that is employed. For example, the EPR for women would be calculated as:\n\nEPRw (%) = 100 x Employed women / Working-age women\n\n\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.\n\n### Development relevance:\nThe notion that employment \u2013 specifically, access to decent work \u2013 is central to poverty reduction was firmly acknowledged in the framework of the Millennium Development Goals (MDGs) with the adoption of an employment-based target under the goal of halving the share of the world\u2019s population living in extreme poverty. The employment-to-population ratio was adopted as one of four indicators to measure progress towards target 1b on \u201cachieving full and productive employment and decent work for all, including women and young people\u201d. After the MDGs came to an end in 2015, the crucial role of decent work in poverty reduction was reinforced in the Sustainable Development Goals (SDGs). In fact, the eighth SDG constitutes the goal of \u201cpromoting inclusive and sustainable economic growth, employment and decent work for all\u201d.\n\n\n\nThe employment-to-population ratio provides information on the ability of an economy to create employment; for many countries the indicator is often more insightful than the unemployment rate. Although a high overall ratio is typically considered as positive, the indicator alone is not sufficient for assessing the level of decent work or decent work deficits. The ratio could be high for reasons that are not necessarily positive \u2013 for example, where education options are limited, young people tend to take up any work available rather than staying in school to build their human capital. For these reasons, it is strongly advised that indicators should be reviewed collectively in any evaluation of country-specific labor market policies.\n\n### Limitations and exceptions:\nData on employment by status are drawn from labor force surveys and household surveys, supplemented by official estimates and censuses for a small group of countries. The labor force survey is the most comprehensive source for internationally comparable employment, but there are still some limitations for comparing data across countries and over time even within a country.\n\nComparability of employment ratios across countries is affected by variations in definitions of employment and population. The biggest difference results from the age range used to define labor force activity. The population base for employment ratios can also vary. Most countries use the resident, non-institutionalized population of working age living in private households, which excludes members of the armed forces and individuals residing in mental, penal, or other types of institutions. But some countries include members of the armed forces in the population base of their employment ratio while excluding them from employment data.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. Employment ratios tend to vary during the year as seasonal workers enter and leave.\n\nThis indicator also has a gender bias because women who do not consider their work employment or who are not perceived as working tend to be undercounted. This bias has different effects across countries and reflects demographic, social, legal, and cultural trends and norms.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.EMP.TOTL.SP.ZS (https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.EMP.TOTL.SP.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Estimates 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Medium_1950-2023.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects - Interim Update", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.\n\nThis is an interim update containing revised medium-variant estimates and projections for Togo.", "title_snapshot": "World Population Prospects - Interim Update (2026-01-19, Togo)", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/WPP2024_CSV_files_update.zip", "date_accessed": "2026-03-31", "date_published": "2026-01-19", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Estimates 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_Population1JanuaryBySingleAgeSex_Medium_1950-2023.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Medium 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_Population1JanuaryBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Low-fertility scenario 1950-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Low_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Medium scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, High-fertility scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_High_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Constant-fertility scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Constant%20fertility_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } } ], "unit": "people", "short_unit": "", "display": { "name": "Number of employed people", "numDecimalPlaces": 0, "tolerance": 0, "unit": "people" }, "processing_level": "major", "presentation": { "topic_tags": [ "Work & Employment" ] }, "description_processing": "We calculated the number of employed people as the employment-to-population ratio (coming from ILO Modelled Estimates via World Bank) multiplied by the population over age 15 (coming from UN World Population Prospects)." }, "number_unemployed": { "title": "Number of unemployed people", "description_short": "Working-age population (ages 15 and over) who are unemployed.", "description_key": "- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.UEM.TOTL.ZS (https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.CACT.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.CACT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Estimates 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Medium_1950-2023.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects - Interim Update", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.\n\nThis is an interim update containing revised medium-variant estimates and projections for Togo.", "title_snapshot": "World Population Prospects - Interim Update (2026-01-19, Togo)", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/WPP2024_CSV_files_update.zip", "date_accessed": "2026-03-31", "date_published": "2026-01-19", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Estimates 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_Population1JanuaryBySingleAgeSex_Medium_1950-2023.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Medium 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_Population1JanuaryBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Low-fertility scenario 1950-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Low_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Medium scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, High-fertility scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_High_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Constant-fertility scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Constant%20fertility_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } } ], "unit": "people", "short_unit": "", "display": { "name": "Number of unemployed people", "numDecimalPlaces": 0, "tolerance": 0, "unit": "people" }, "processing_level": "major", "presentation": { "topic_tags": [ "Work & Employment" ] }, "description_processing": "We calculated the number of unemployed people as the unemployment rate (coming from ILO Modelled Estimates via World Bank) multiplied by the labor force. The labor force was calculated as the labor force participation rate (coming from ILO Modelled Estimates via World Bank) multiplied by the population over age 15 (coming from UN World Population Prospects)." }, "number_out_of_labor_force": { "title": "Number of people out of the labor force", "description_short": "Working-age population (ages 15 and over) who are not economically active (not employed and not unemployed).", "description_from_producer": "Labor force participation rate is the proportion of the population ages 15 and older that is economically active: all people who supply labor for the production of goods and services during a specified period.\n\n### Aggregation method:\nWeighted average\n\n### Statistical concept and methodology:\nMethodology: The labor force participation rate is calculated by expressing the number of persons in the labor force as a percentage of the population of a given age group. The labor force is the sum of the number of persons employed and the number of unemployed.\n\nLabor force surveys are typically the preferred source of information for determining the labor force participation rate. Such surveys can be designed to cover virtually the entire noninstitutional population of a given country, all branches of economic activity, all sectors of the economy and all categories of workers, including the self-employed, contributing (unpaid) family workers, casual workers and multiple jobholders. In addition, such surveys generally provide an opportunity for the simultaneous measurement of the employed, the unemployed and persons outside the labor force in a coherent framework.\n\nPopulation censuses are another major source of data on the labor force and its components. The labor force participation rates obtained from population censuses, however, tend to be lower, as the vastness of the census operation inhibits the recruitment of trained interviewers and does not typically allow for detailed probing on the labor market activities of the respondents.\n\nThe series is part of the \"ILO modeled estimates database,\" including nationally reported observations and imputed data for countries with missing data, primarily to capture regional and global trends with consistent country coverage. Country-reported microdata is based mainly on nationally representative labor force surveys, with other sources (e.g., household surveys and population censuses) considering differences in the data source, the scope of coverage, methodology, and other country-specific factors. Country analysis requires caution where limited nationally reported data are available. A series of models are also applied to impute missing observations and make projections. However, imputed observations are not based on national data, are subject to high uncertainty, and should not be used for country comparisons or rankings. For more information: https://ilostat.ilo.org/resources/concepts-and-definitions/ilo-modelled-estimates/\nStatistical concept(s): The labor force is the supply of labor available for producing goods and services in an economy. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.\n\n### Development relevance:\nThe labor force participation rate indicator plays a central role in the study of the factors that determine the size and composition of a country\u2019s human resources and in making projections of the future supply of labor. The information is also used to formulate employment policies, to determine training needs and to calculate the expected working lives of the male and female populations and the rates of accession to, and retirement from, economic activity \u2013 crucial information for the financial planning of social security systems.\n\n### Limitations and exceptions:\nData on the labor force are compiled by the ILO from labor force surveys, censuses, and establishment censuses and surveys. For some countries a combination of these sources is used. Labor force surveys are the most comprehensive source for internationally comparable labor force data. They can cover all non-institutionalized civilians, all branches and sectors of the economy, and all categories of workers, including people holding multiple jobs. By contrast, labor force data from population censuses are often based on a limited number of questions on the economic characteristics of individuals, with little scope to probe. The resulting data often differ from labor force survey data and vary considerably by country, depending on the census scope and coverage. Establishment censuses and surveys provide data only on the employed population, not unemployed workers, workers in small establishments, or workers in the informal sector.\n\nThe reference period of a census or survey is another important source of differences: in some countries data refer to people's status on the day of the census or survey or during a specific period before the inquiry date, while in others data are recorded without reference to any period. In countries, where the household is the basic unit of production and all members contribute to output, but some at low intensity or irregularly, the estimated labor force may be much smaller than the numbers actually working.\n\nDiffering definitions of employment age also affect comparability. For most countries the working age is 15 and older, but in some countries children younger than 15 work full- or part-time and are included in the estimates. Similarly, some countries have an upper age limit. As a result, calculations may systematically over- or underestimate actual rates.\n\n### Other notes:\nNational estimates are also available in the WDI database. Caution should be used when comparing ILO estimates with national estimates.", "description_key": "- This data comes from the ILO Modelled Estimates series. [The International Labour Organization (ILO)](#dod:ilo) combines countries' own reported estimates with statistically modeled estimates when observations are missing. This improves comparability across countries and over time and allows the ILO to calculate regional and global aggregates for every year. You can read more about how the ILO produces these estimates in the [Modelled Estimates documentation](https://ilostat.ilo.org/methods/concepts-and-definitions/ilo-modelled-estimates/).\n- This data follows the standards of the [13th International Classification of Labour Statisticians (ICLS)](#dod:13-icls). Under this framework, employment includes work for pay or profit, including self-employment, as well as the production of goods for own use (such as subsistence farming). Changes in the definition of employment also affect who is counted as unemployed or outside the labor force. Because definitions were updated under the [19th ICLS](#dod:19-icls), data using the newer definitions is not fully comparable with data based on the 13th ICLS. You can read more about the definitions in [this explainer by the ILO](https://www.ilo.org/publications/quick-guide-understanding-impact-new-statistical-standards-ilostat).", "origins": [ { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Estimates 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Medium_1950-2023.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects - Interim Update", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.\n\nThis is an interim update containing revised medium-variant estimates and projections for Togo.", "title_snapshot": "World Population Prospects - Interim Update (2026-01-19, Togo)", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/WPP2024_CSV_files_update.zip", "date_accessed": "2026-03-31", "date_published": "2026-01-19", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Estimates 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_Population1JanuaryBySingleAgeSex_Medium_1950-2023.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Medium 1950-2023", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_Population1JanuaryBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Low-fertility scenario 1950-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Low_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Medium scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Medium_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, High-fertility scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_High_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "United Nations", "title": "World Population Prospects", "description": "World Population Prospects 2024 is the 28th edition of the official estimates and projections of the global population that have been published by the United Nations since 1951. The estimates are based on all available sources of data on population size and levels of fertility, mortality and international migration for 237 countries or areas. If you have questions about this dataset, please refer to [their FAQ](https://population.un.org/wpp/faqs). You can also explore [data sources](https://population.un.org/wpp/data-sources) for each country or visit [their main page](https://population.un.org/wpp/) for more details.", "title_snapshot": "World Population Prospects, Population, Constant-fertility scenario 2024-2100", "citation_full": "United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition.", "attribution": "UN, World Population Prospects (2024)", "attribution_short": "UN WPP", "url_main": "https://population.un.org/wpp/downloads/", "url_download": "https://population.un.org/wpp/assets/Excel%20Files/1_Indicator%20(Standard)/CSV_FILES/WPP2024_PopulationBySingleAgeSex_Constant%20fertility_2024-2100.csv.gz", "date_accessed": "2024-07-11", "date_published": "2024-07-11", "license": { "name": "CC BY 3.0 IGO", "url": "https://population.un.org/wpp/downloads/" } }, { "producer": "ILO Modelled Estimates, via World Bank", "title": "World Development Indicators", "description": "The World Development Indicators (WDI) database, published by the World Bank, is a comprehensive collection of global development data, providing key economic, social, and environmental statistics. It includes over 1,500 indicators covering more than 200 countries and territories, with data spanning several decades. WDI serves as a vital resource for policymakers, researchers, businesses, and analysts seeking to understand global trends and make data-driven decisions. The database covers a wide range of topics, including economic growth, education, health, poverty, trade, energy, infrastructure, governance, and environmental sustainability. The indicators are sourced from reputable national and international agencies, ensuring high-quality, consistent, and comparable data. Users can access the database through interactive online tools, API services, and downloadable datasets, facilitating detailed analysis and visualization. WDI is also used for tracking progress on the Sustainable Development Goals (SDGs) and other global development initiatives. By providing accessible and reliable statistics, it helps to inform policy discussions and strategies globally. Whether for academic research, policy planning, or economic analysis, the World Development Indicators database is an essential tool for understanding and addressing global development challenges.", "citation_full": "ILO Modelled Estimates database (ILOEST), International Labour Organization (ILO), uri: https://ilostat.ilo.org/data/bulk/, publisher: ILOSTAT, type: external database, date accessed: January 17, 2026. Indicator SL.TLF.CACT.ZS (https://data.worldbank.org/indicator/SL.TLF.CACT.ZS). World Development Indicators - World Bank (2026). Accessed on 2026-07-14.", "version_producer": "129", "url_main": "https://data.worldbank.org/indicator/SL.TLF.CACT.ZS", "url_download": "https://databankfiles.worldbank.org/public/ddpext_download/WDI_CSV.zip", "date_accessed": "2026-07-14", "date_published": "2026-07-01", "license": { "name": "CC BY 4.0", "url": "https://datacatalog.worldbank.org/search/dataset/0037712/World-Development-Indicators" } } ], "unit": "people", "short_unit": "", "display": { "name": "Number of people out of the labor force", "numDecimalPlaces": 0, "tolerance": 0, "unit": "people" }, "processing_level": "major", "presentation": { "topic_tags": [ "Work & Employment" ] }, "description_processing": "We calculated the number of people out of the labor force as the total population (coming from UN World Population Prospects) minus the labor force and the population below working age (also from UN World Population Prospects). The labor force was calculated as the labor force participation rate (coming from ILO Modelled Estimates via World Bank) multiplied by the population over age 15 (coming from UN World Population Prospects)." }, "population": { "title": "Population", "description": "Population by country and year.", "description_short": "Population by country, available from 10,000 BCE to 2100, based on data and estimates from different sources.", "origins": [ { "producer": "Various sources", "title": "Population", "description": "Our World in Data builds and maintains a long-run dataset on population by country, region, and for the world, based on various sources.\n\nYou can find more information on these sources and how our time series is constructed on this page: https://ourworldindata.org/population-sources", "citation_full": "The long-run data on population is based on various sources, described on this page: https://ourworldindata.org/population-sources", "attribution": "Population based on various sources (2024)", "attribution_short": "Population", "url_main": "https://ourworldindata.org/population-sources", "date_accessed": "2026-03-31", "date_published": "2024-07-15", "license": { "name": "CC BY 4.0", "url": "https://creativecommons.org/licenses/by/4.0/" } } ], "unit": "people", "short_unit": "", "display": { "isProjection": false, "roundingMode": "significantFigures", "numSignificantFigures": 4, "unit": "people" }, "processing_level": "minor" } } }